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Univariate Stock Market Forecasting using Facebook Prophet in Python

Florian Follonier — Thu, 15 Dec 2022 22:54:34 +0000

Have you ever wondered how Facebook predicts the future? Meet Facebook Prophet, the open-source time series forecasting tool developed by Facebook’s Core Data Science team. Built on top of the PyStan library, Facebook Prophet offers a simple and intuitive interface for creating forecasts using historical data. What sets Facebook Prophet apart is its highly modular design, allowing for a range of customizable components that can be combined to create a wide variety of forecasting models. This makes it perfect for modeling data with strong seasonal effects, like daily or weekly patterns, and it can handle missing data and outliers with ease. In this tutorial, we will take a closer look at the capabilities of Facebook Prophet and see how it can be used to make accurate predictions.

We begin with a brief discussion of how the Facebook Prophet decomposes a time series into different components. Then we turn to the hands-on part. you can use its model in Python to generate a stock market forecast. We will train our Facebook Prophet model using the historical price of the Coca-Cola stock. We will also cover different options to customize the model settings.

Disclaimer: This article does not constitute financial advice. Stock markets can be very volatile and are generally difficult to predict. Predictive models and other forms of analytics applied in this article only illustrate machine learning use cases.

Facebook Prophet – an open-source tool for time series forecasting

What is Facebook Prophet?

Facebook Prophet is a tool that can be used to make predictions about future events based on historical data. It was developed by Taylor and Letham, 2017, who later made it available as an open-source project. The authors developed Facebook Prophet to solve various business forecasting problems without requiring much prior knowledge. In this way, the framework addresses a significant problem many companies face today. They have various prediction problems (e.g., capacity and demand forecasting) but face a skill gap when it comes to generating reliable forecasts with techniques such as ARIMA or neural networks. Compared to that, Facebook Prophet requires minimal fine-tuning and can deal with various challenges, including seasonality, outliers, and changing trend lines. This allows Facebook Prophet to handle a wide range of forecasting problems flexibly. Before we dive into the hands-on part, let’s gain a quick overview of how Facebook Prophet works.

Also: Stock Market Prediction using Multivariate Time Series

time series forecasting with facebook prophet python tutorial

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time series forecasting with facebook prophet python tutorial

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Time-series forecasting with Facebook Prophet. Image generated with Midjourney.

How Facebook Prophet Works

Facebook Prophet uses a technique called additive regression to model time series data. This involves breaking the time series into a series of components:

Trends
Seasonality
Holiday

Traditional time series methods such as (S)ARIMA base their prediction on a model that weights the linear sum of past observations or lags. Facebook’s Prophet is similar in that it uses a decreasing weight for past observations. This means current observations have a higher significance for the model than those that date back a long time. It then models each component separately using a combination of linear and non-linear functions. Finally, Facebook Prophet combines these components to form the complete forecast model. Let’s take a closer look at these components and how Facebook Prophet handles them.

A) Dealing with Trends

Time series often have a trendline. However, even more often, a time series will not follow a single trend, but it has several trend components that are separated by breakpoints. Facebook Prophet tries to handle these trends in several ways. First, the model tries to identify the breakpoints (knots) in a time series that divide different periods. Each breakpoint separates two periods with different trendlines. Facebook Prophet then uses these inflection points between periods to fit the model to the data and create the forecast. In addition, trendlines do not have to be linear but can also be logarithmic. This is all done automatically, but it is also possible to specify breakpoints manually.

B) Seasonality

Facebook Prophet works very well when the data shows a strong seasonal pattern. It uses Fourier transformations (adding different sine and cosine frequencies) to account for daily, weekly and yearly seasonality. The Facebook Prophet model is flexible on the type of data you have by allowing you to adjust the seasonal components of your data. By default, Facebook Prophet assumes daily data with weekly and yearly seasonal effects. If your data differentiates from this standard, for example, you have weekly data with monthly seasonality, then you need to adjust the number of terms accordingly.

C) Holiday

Every year, public holidays can lead to strong deviations in a time series; for example, thinking of computing power, demand more people will visit the Facebook website. The Facebook Prophet model also accounts for such special events by allowing us to specify binary indicators that mark whether a certain day is a public holiday. If you have other non-holiday events that occur yearly, you can use this indicator for the same purpose. Usually, Facebook Prophet will automatically remove outliers from the data. But if an outlier occurs on a day highlighted as a public holiday, Facebook Prophet will adjust its model accordingly.

Hyperparameter Tuning and Customization

Facebook Prophet includes additional optimization techniques, such as Bayesian optimization, to automatically tune the model’s hyperparameters, such as the length of the seasonal period, to improve its accuracy. Once the model is trained, it can be used to predict future values in the time series. However, users with a strong domain knowledge may prefer to tweak these parameters themselves, and Facebook Prophet provides several functions for this purpose. It also includes a range of tools for model evaluation and diagnostics, as well as for visualizing the model and the input data.

Also: Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python

Application Domains

Facebook Prophet is a powerful forecasting tool that has been specifically designed to make forecasting easy. As mentioned, Prophet is easy to use and can flexibly handle various forecasting problems. In addition, it requires very little preprocessing to generate accurate forecasts. As a result of these advantages, Facebook Prophet has been adopted by various application domains. Some possible application domains for Facebook Prophet include:

Sales forecasting: Facebook Prophet can be used to predict future sales of a product or service, based on historical sales data. This can be useful for businesses to plan their inventory and staffing, and to make informed decisions about future investments and growth.
Financial forecasting: Facebook Prophet can be used to predict future stock prices, currency exchange rates, or other financial metrics. This can be useful for investors and financial analysts to make informed decisions about the market.
Traffic forecasting: Facebook Prophet can be used to predict future traffic on a website or mobile app based on historical data. This can be useful for businesses to plan for capacity and optimize their servers and infrastructure.
Energy consumption forecasting: Facebook Prophet can be used to predict future energy consumption based on historical data. This can be useful for utilities and energy companies to plan for demand and optimize their generation and distribution.

When to Use Facebook Prophet?

Although Facebook Prophet is applicable in any domain where time series data is available, it is most effective when certain conditions are met. These include univariate time series data with prominent seasonal effects and an extensive historical record spanning multiple seasons. Facebook Prophet is especially beneficial when dealing with large quantities of historical data that require efficient analysis and quick, accurate predictions of future trends.

Also: Rolling Time Series Forecasting: Creating a Multi-Step Prediction

Using Facebook Prophet to Forecast the Coca-Cola Stock Price in Python

In this hands-on tutorial, we’ll use Facebook Prophet and Python to create a forecast for the Coca-Cola stock price. We have chosen Coca-Cola as an example because the Coca-Cola share is known to be a cyclical stock. As such, its chart reflects a seasonal pattern, different periods, and varying trend lines. We train our model on historical price data and then predict the next data points half-year in advance. In addition, we will discuss how we could finetune our model to improve the accuracy of the predictions further. This involves the following steps:

Collect historical stock data for CocaCola and familiarize ourselves with the data.
Use Facebook Prophet to fit a model to the data.
Use the model to make predictions about the future stock price of Coca-Cola.
Visualize model components and predictions.
Manually adjust the model to improve the model fit.

By following these steps, we will try to gain insights into the future performance of Coca-Cola stock. Let’s get started!

As always, you can find the code of this tutorial on the GitHub repository.

View on GitHub Relataly GitHub Repo

Prerequisites

Before you proceed, ensure that you have set up your Python environment (3.8 or higher) and the required packages. If you don’t have an environment, consider following this tutorial to set up the Anaconda environment.

Also, make sure you install all required Python packages. We will be working with the following standard Python packages:

pandas
seaborn
matplotlib

In addition, we will use the Facebook Prophet library that goes by the library name “prophet.” You can install these packages using the following commands:

pip install 
conda install  (if you are using the anaconda packet manager)

Step #1 Loading Packages and API Key

Let’s begin by loading the required Python packages and historical price quotes for the Coca-Cola stock. We will obtain the data from the yahoo finance API. Note that the API will return several columns of data, including, opening, average, and closing prices. We will only use the closing price.

# Tested with Python 3.8.8, Matplotlib 3.5, Seaborn 0.11.1, numpy 1.19.5, plotly 4.1.1, cufflinks 0.17.3, prophet 1.1.1, CmdStan 2.31.0
import pandas as pd 
import matplotlib.pyplot as plt 
import numpy as np 
from math import log, exp 
from datetime import date, timedelta, datetime
import seaborn as sns
sns.set_style('white', {'axes.spines.right': False, 'axes.spines.top': False})
from scipy.stats import norm
from prophet import Prophet
from prophet.plot import add_changepoints_to_plot
import cmdstanpy
cmdstanpy.install_cmdstan()
cmdstanpy.install_cmdstan(compiler=True)
# Setting the timeframe for the data extraction
end_date =  date.today().strftime("%Y-%m-%d")
start_date = '2010-01-01'
# Getting quotes
stockname = 'Coca Cola'
symbol = 'KO'
# You can either use webreader or yfinance to load the data from yahoo finance
# import pandas_datareader as webreader
# df = webreader.DataReader(symbol, start=start_date, end=end_date, data_source="yahoo")
import yfinance as yf #Alternative package if webreader does not work: pip install yfinance
df = yf.download(symbol, start=start_date, end=end_date)
# Quick overview of dataset
print(df.head())

[*********************100%***********************]  1 of 1 completed
                 Open       High        Low      Close  Adj Close    Volume
Date                                                                       
2010-01-04  28.580000  28.610001  28.450001  28.520000  19.081614  13870400
2010-01-05  28.424999  28.495001  28.070000  28.174999  18.850786  23172400
2010-01-06  28.174999  28.219999  27.990000  28.165001  18.844103  19264600
2010-01-07  28.165001  28.184999  27.875000  28.094999  18.797268  13234600
2010-01-08  27.730000  27.820000  27.375000  27.575001  18.449350  28712400

Once we have downloaded the data, we create a line plot of the closing price to familiarize ourselves with the time series data. Note that Facebook Prophet works on a single input signal only (univariate data). This input will be the closing price. For illustration purposes, we add a moving average to the chart. However, the moving average makes it easier to spot trends and seasonal patterns, it will not be used to fit the model.

# Visualize the original time series
rolling_window=25
y_a_add_ma = df['Close'].rolling(window=rolling_window).mean() 
fig, ax = plt.subplots(figsize=(20,5))
sns.lineplot(data=df, x=df.index, y='Close', color='skyblue', linewidth=0.5, label='Close')
sns.lineplot(data=df, x=df.index, y=y_a_add_ma, 
    linewidth=1.0, color='royalblue', linestyle='--', label=f'{rolling_window}-Day MA')

The chart shows a long-term upward trend interrupted by phases of downturns. In addition, between 2010 and 2018, we can see some cyclical movements. At some points, we can spot clear breakpoints, for example, in 2019 and mid-2020.

Step #2 Preparing the Data

Next, we prepare our data for model training. Propjet has a strict condition on how the input columns must be named. In order to use Facebook Prophet, your data needs to be in a time series format with the time as the index and the value as the first column. In addition, column names need to adhere to the following naming convention:

ds for the timestamp
y for the metric columns, which in our case is the closing price

So before we proceed, we must rename the columns in our dataframe. In addition, we will remove the index and drop NA values.

df_x = df[['Close']].copy()
df_x['ds'] = df.index.copy()
df_x.rename(columns={'Close': 'y'}, inplace=True)
df_x.reset_index(inplace=True, drop=True)
df_x.dropna(inplace=True)
df_x.tail(9)

		y			ds
3257	63.139999	2022-12-09
3258	63.970001	2022-12-12
3259	63.990002	2022-12-13

Now we have a simple dataframe with ds and y as the only variables.

Step #3 Model Fitting and Forecasting

Next, let’s fit our forecasting model to the time series data. Afterward, we can make predictions about future values in the series. However, before we do this, we need to define our prediction interval.

3.1 Setting the Prediction Interval

The prediction interval is a measure of uncertainty in a forecast made with Facebook Prophet. It indicates the range within which the true value of the forecasted quantity is expected to fall a certain percentage of the time. For example, a 95% prediction interval means that the true value of the forecasted quantity is expected to fall within the given range 95% of the time.

In Facebook Prophet, the prediction interval is controlled by the interval_width parameter, which can be set when calling the predict method. The default value for interval_width is 0.80. This means that the true value of the forecasted quantity is expected to fall within the prediction interval 80% of the time. We can adjust the value of interval_width to change the width of the prediction interval as desired. In the example below, we use a prediction interval of 0.85.

3.2 Fit the Model

Next, let’s fit our model and generate a one-year forecast. First, we need to instantiate our model with by calling Prophet(). Then we use model.fit(df) to fit this model to the historical price quotes of the Coca-Cola stock. Once, we have done that, we use the model instance model.make_future_dataframe() to create an extended dataframe (future_df). This dataframe has been extended with records for a one-year period. The records are empty dummy values ready to be filled with the real forecast. We then pass this dummy dataframe to the model.predict(df) function, Facebook Prophet creates the forecast and fills up the dummy dataframe with the forecast values.

For the sake of reusability, I have encapsulated the entire process into a wrapper function. This will allow us to run quick experiments with different parameter values.

# This function fits the prophet model to the input data and generates a forecast
def fit_and_forecast(df, periods, interval_width, changepoint_range=0.8):
    # set the uncertainty interval
    Prophet(interval_width=interval_width)
    # Instantiate the model
    model = Prophet(changepoint_range=changepoint_range)
    # Fit the model
    model.fit(df)
    # Create a dataframe with a given number of dates
    future_df = model.make_future_dataframe(periods=periods)
    # Generate a forecast for the given dates
    forecast_df = model.predict(future_df)
    #print(forecast_df.head())
    return forecast_df, model, future_df
# Forecast for 365 days with full data
forecast_df, model, future_df = fit_and_forecast(df_x, 365, 0.95)
print(forecast_df.columns)
forecast_df[['yhat_lower', 'yhat_upper', 'yhat']].head(5)

Index(['ds', 'trend', 'yhat_lower', 'yhat_upper', 'trend_lower', 'trend_upper',
       'additive_terms', 'additive_terms_lower', 'additive_terms_upper',
       'weekly', 'weekly_lower', 'weekly_upper', 'yearly', 'yearly_lower',
       'yearly_upper', 'multiplicative_terms', 'multiplicative_terms_lower',
       'multiplicative_terms_upper', 'yhat'],
      dtype='object')
	yhat_lower	yhat_upper	yhat
0	24.468273	28.944286	26.691615
1	24.496074	29.146425	26.706924
2	24.513424	28.829159	26.682213
3	24.358048	28.767209	26.667476
4	24.487963	28.839966	26.666242

Voila, we have generated a one-year forecast.

Step #4 Analyzing the Forecast

Next, let’s visualize our forecast and discuss what we see. The most simple way is to create the plot with a standard Facebook Prophet function.

Also: Measuring Regression Errors with Python

model.plot(forecast_df, uncertainty=True)

So what do we see? The forecast shows that our model does not simply predict a straight line and instead has generated a more sophisticated forecast that displays an upward cyclical trend with higher highs and higher lows.

The black dots are the data points from the historical data to which we have fit our model.
The dark blue line is the most likely path.
The light blue lines are the upper and lower boundaries of the prediction interval. We have set the prediction interval to 0.85, which means there is a probability of 85% the actual values will fall into this range.
In total, the model seems confident that the price of Coca-Cola stock will rise within the next year (no financial advice). However, as we will see later, the forecast depends on where the model sees the breakpoints.

In case, you want to create a custom plot, you can use the function below.

# Visualize the Forecast
def visualize_the_forecast(df_f, df_o):
    rolling_window = 20
    yhat_mean = df_f['yhat'].rolling(window=rolling_window).mean() 
    # Thin out the ground truth data for illustration purposes
    df_lim = df_o
    # Print the Forecast
    fig, ax = plt.subplots(figsize=[20,7])
    sns.lineplot(data=df_f, x=df_f.ds, y=yhat_mean, ax=ax, label='predicted path', color='blue')
    sns.lineplot(data=df_lim, x=df_lim.ds, y='y', ax=ax, label='ground_truth', color='orange')
    #sns.lineplot(data=df_f, x=df_f.ds, y='yhat_lower', ax=ax, label='yhat_lower', color='skyblue', linewidth=1.0)
    #sns.lineplot(data=df_f, x=df_f.ds, y='yhat_upper', ax=ax, label='yhat_upper', color='coral', linewidth=1.0)
    plt.fill_between(df_f.ds, df_f.yhat_lower, df_f.yhat_upper, color='lightgreen')
    plt.legend(framealpha=0)
    ax.set(ylabel=stockname + " stock price")
    ax.set(xlabel=None)
visualize_the_forecast(forecast_df, df_x)

Step #5 Analyzing Model Components

We can gain a better understanding of different model components by using the plot_components function. This method creates a plot showing the trend, weekly and yearly seasonality, and any additional user-defined seasonalities of the forecast. This can be useful for understanding the underlying patterns in the data and for diagnosing potential issues with the model.

model.plot_components(forecast_df)

The first chart shows the trendlines that the model sees within different periods. The trendlines are separated by breakpoints about, which we will talk in the next section. When we look at the second plot, we can see no price changes during the weekend. This is plausible, considering that the stock markets are closed over the weekend. The third chart is most interesting, as it shows that the model has recognized some yearly seasonality with two peaks in April and August, as well as lows in March and October.

Step #6 Adjusting the Changepoints of our Facebook Prophet Model

Let’s take a closer look at the changepoints in our model. Changepoints are the points in time where the trend of the time series is expected to change, and Facebook Prophet’s algorithm automatically detects these points and adapts the model accordingly. Changepoints are important to Facebook Prophet because they allow the model to capture gradual changes or shifts in the data. By identifying and incorporating changepoints into the forecasting model, Facebook Prophet can make more accurate predictions. Changepoints can also help to identify potential outliers in the data.

6.1 Checking Current Changepoints

We can illustrate the changepoints in our model with the add_changepoints_to_plot method. The method adds vertical lines to a plot to indicate the locations of the changepoints in the data. By plotting the changepoints on a graph, we can visually identify when these changes in trend occur and potentially diagnose any issues with our model.

# Printing the ChangePoints of our Model
forecast_df, model, future_df = fit_and_forecast(df_x, 365, 1.0)
axislist = add_changepoints_to_plot(model.plot(forecast_df).gca(), model, forecast_df)

The chart above shows that our model has identified several changepoints in the historical data. However, it has only searched for changepoints within 80% of the time series. As a result, the algorithm hasn’t identified any change points in the most recent years after 2020. We can adjust the changepoints with the changepoint_range (default = 80%) variable. This is what we will do in the next section.

6.2 Adjusting Changepoints

We can adjust the range within which Facebook Prophet looks for changepoints with the “changepoint_range”. It is specified as a fraction of the total duration of the time series. For example, if changepoint_range is set to 0.8 and the time series spans 10 years, the algorithm will look for changepoints within the last 8 years of the series.

By default, changepoint_range is set to 0.8, which means that the algorithm will look for changepoints within the last 80% of the time series. We can adjust this value depending on the characteristics of our data and our desired level of flexibility in the model.

Increasing the value of changepoint_range will allow the algorithm to identify more changepoints and potentially improve the fit of the model, but it may also increase the risk of overfitting. Conversely, decreasing the value of changepoint_range will reduce the number of changepoints detected and may improve the model’s ability to generalize to new data, but it may also reduce the accuracy of the forecast.

Let’s fit our model again, but this time we let Facebook Prophet search for changepoints within the entire time series (changepoint_range=1.0).

# Adjusting ChangePoints of our Model
forecast_df, model, future_df = fit_and_forecast(df_x, 365, 1.0, 1.0)
axislist = add_changepoints_to_plot(model.plot(forecast_df).gca(), model, forecast_df)

The plot above shows that Facebook Prophet has now identified several additional breakpoints in the time series. As a result, the forecast has become rather pessimistic, as Facebook Prophet gave more weight to recent changes.

Finally, it is worth mentioning that it is possible to add changepoints for specific dates manually. You can try this out using “model.changepoints(series)”. The function takes a series of timestamps as the parameter value.

Summary

Get ready to dive into the world of stock market prediction with Facebook Prophet! In this article, we’ll show you how to leverage the power of this amazing tool to forecast time series data, using Coca-Cola’s stock as an example. We’ll guide you through the process of fitting a curve to univariate time series data and fine-tuning the initial breakpoints and trendlines to enhance model performance. With Facebook Prophet’s automatic trend identification algorithm, you’ll be able to easily adapt to changes in the data over time.

Also: Mastering Multivariate Stock Market Prediction with Python

As a data scientist, you’ll appreciate how easy it is to use Facebook Prophet and how it consistently outperforms other models. With its straightforward interface and impressive accuracy, this tool is a must-have for your forecasting toolkit. And we’re always looking for feedback from our audience, so let us know what you think! We’re committed to improving our content to provide the best learning experience possible.

Sources and Further Reading

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Other Methods for Time Series Forecasting

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Unveiling Hidden Patterns in the Cryptocurrency Market with Affinity Propagation and Python

Florian Follonier — Mon, 02 May 2022 18:34:02 +0000

Affinity propagation is a powerful unsupervised clustering technique that can identify hidden patterns in large datasets. In the cryptocurrency world, where new coins are constantly emerging and prices can be highly volatile, affinity propagation can help investors simplify the chaos.

By analyzing historical price data, affinity propagation groups coins into clusters based on their past price fluctuations. Such a cluster analysis enables crypto investors to identify promising entry and exit points, ultimately helping them make smarter investment decisions.

To use this technique effectively, it’s important to understand essential concepts such as covariance, lasso regression, and affinity propagation. Once you understand these concepts, you can apply them to analyze price time series data and identify hidden patterns.

Finally, visualizing the results in two and three dimensions can better understand the relationships between coins and their respective clusters. The resulting crypto market map can be a powerful tool for investors to gain insight into the market’s structure and make informed investment decisions.

Disclaimer

This article does not constitute financial advice. Stock markets can be very volatile and are generally difficult to predict. Predictive models and other forms of analytics applied in this article only serve the purpose of illustrating machine learning use cases.

What is Stock Market Clustering?

Clustering stock markets refers to grouping stocks based on their similarities or common characteristics. This can be done using various clustering algorithms, which analyze the data and assign each stock market to a cluster based on its similarity to other stock markets in the same cluster. In this article, we will run a cluster analysis on historical time series data. This approach involves grouping stocks into clusters based on their historical performance over a certain period of time.

Clustering stock market data can be useful for a variety of purposes, such as identifying patterns or trends in the data, comparing the performance of different stocks or sectors, or generating investment recommendations. However, it’s important to keep in mind that clustering is just one tool among many for analyzing stock market data, and it’s important to consider a range of factors when making investment decisions. It can also be used to compare the performance of different stock markets and identify potential risks or correlations between them.

Also: Color-Coded Cryptocurrency Price Charts in Python

neural network machine learning python affinity propagation midjourney relataly crypto-min

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We can use a crypto market map to illustrate the price correlation between cryptocurrencies.

What’s the Problem with Prototype-based Clustering?

Clustering is an unsupervised learning technique that groups similar objects into clusters and separates them from different ones. One of the most popular clustering techniques is k-means. K-means belongs to the so-called prototype-based clustering techniques, which divide data points into a predefined number of groups (in the case of k-means, the groups are of equal variance).

The prototype-based clustering approach works great if the number of clusters in a dataset is known and the clusters have similar despair. However, when we deal with real-world problems, we often encounter more complex data for which the optimal number of clusters is unknown and difficult or even impossible to guess. In such a case, affinity propagation has a significant advantage because it can automatically estimate the number of clusters.

Affinity Propagation: What it is and How it Works

The idea of affinity propagation is to identify clusters by measuring the similarity of data points relative to one another. The algorithm chooses data points as cluster centers that best represent other data points near them.

We can imagine the process of identifying these representative data points as an election. Each data point (i) is a voter who casts votes and a candidate (k) who can receive votes from other voters. Votes are a measure of the similarity of data points. A voter who gives many votes to a candidate expresses that this data point is similar to him and therefore is suitable for representing him as a cluster center. The voting process continues until the algorithm reaches a consensus and selects a set number of cluster candidates.

Affinity Propagation: Data points cast votes for candidates and receive votes from other data points

The clustering process involves many separate steps (This article provides a detailed description of the steps involved) and works with several matrices:

The similarity matrix assesses the suitability of data points (candidates) to act as cluster centers.
The availability matrix (or responsibility matrix) collects the support of the data points for the candidates (potential cluster centers) and their suitability to represent them.
The criterion matrix sums up the results and defines the clusters. Data points with equal scores in the criterion matrix are considered part of the same cluster.

Criterion Matrix: Data Points (Cryptos) with equal numbers are part of the same cluster

Time Series Clustering using Affinity Propagation – Visualizing Cryptocurrency Market Structures in Python

Ready to implement affinity propagation in Python to analyze the crypto market structure and create a visual representation of price similarity? Let’s dive in!

First, we define a portfolio of cryptocurrencies and download their historical price quotes from coinmarketcap. We then visualize the time series on separate line charts to ensure that the data has been loaded successfully. After preparing and cleaning the data, we can move on to clustering the cryptocurrencies into groups with similar price movements using Affinity Propagation.

Unlike other clustering algorithms, we don’t set the number of clusters in advance. Instead, we let affinity propagation determine the optimal number of clusters for our portfolio. Finally, we calculate the covariance matrix between clusters and arrange the cryptocurrencies on a 2D map into clusters. We create a network overlay based on covariance to better understand the relationships between different clusters.

With affinity propagation, we can identify hidden patterns in the crypto market and group coins into clusters based on their past price fluctuations. This process allows us to identify promising entry and exit points, ultimately helping us make smarter investment decisions. Plus, the 2D map and network overlay help us visualize the relationships between different clusters and coins.

We can use affinity propagation to cluster financial assets and visualize them on a map.

The Python code for this tutorial is available in the relataly repository on GitHub.

View on GitHub Relataly GitHub Repo

Prerequisites

Before beginning the coding part, ensure that you have set up your Python 3 environment and required packages. Consider Anaconda if you don’t have a Python environment set up yet. To set it up, you can follow the steps in this tutorial. Also, make sure you install all required packages. In this tutorial, we will be working with the following standard packages:

Please also make sure you have the Cmcscaper package installed. We will be using it to download past crypto prices from coinmarketcap.

You can install these packages using console commands:

pip install
conda install (if you are using the anaconda packet manager)

Step #1: Load the Stock Market Data

We start by loading historical crypto price data from Coinmarketcap. To download the data, we use Cmcscraper, a Python library that allows us to collect Coinmarketcap data without signing up for the official API.

The download returns a dataframe with daily price quotes (Close, Open, Avg) for cryptocurrencies between 2016 and today. You can use the dictionary (“symbol_dict”) to control which cryptos you want to include in the data. We limit the data we use in our cluster analysis to the last 50 days. In this way, we let the correlation consider earlier price developments. But it’s up to you to specify a different period. In addition, instead of using absolute price values, we will use daily percentage fluctuations.

Also: Requesting Crypto Price Data from the Gate.io REST API in Python

Loading the data can take several minutes, depending on how many cryptocurrencies we include in the request. So it makes sense not to load the data every time you run the code. Therefore, the code below stores the historical prices in a CSV file.

The script will check if the data already exists if you run the code below. If it does, it will use the data from the CSV file. Otherwise, it will load a fresh copy of the data from coinmarketcap.

# A tutorial for this file is available at www.relataly.com
# Tested with Python 3.8.8, Matplotlib 3.5, Scikit-learn 0.24.1, Seaborn 0.11.1, numpy 1.19.5

from cryptocmd import CmcScraper
import pandas as pd 
import matplotlib.pyplot as plt 
import numpy as np 
import seaborn as sns
from sklearn import cluster, covariance, manifold
import requests
import json


#get a dictionary of the top 100 coin symbols and names from an API
def get_symbol_dict():
    url = 'https://api.coinmarketcap.com/data-api/v3/cryptocurrency/listing?start=1&limit=50&sortBy=market_cap&sortType=desc&convert=USD&cryptoType=all&tagType=all&audited=false'
    response = requests.get(url)
    data = json.loads(response.text)
    df = pd.DataFrame(data['data']['cryptoCurrencyList'])

    # exclude stable coins
    df = df[~df['symbol'].isin(['USDT', 'USDC', 'BUSD', 'DAI', 'TUSD', 'PAX', 'GUSD', 'HUSD', 'USDK', 'USDS', 'USDP', 'USDN', 'USDSB', 'USDX', 'USD++', 'BIDR', 'IDRT', 'VAI', 'BGBP'])]
    df = df[['symbol', 'name']]
    df = df.set_index('symbol')
    df = df.to_dict()
    df = df['name']
    return df

symbol_dict = get_symbol_dict()


# Download historic crypto prices via CmcScraper
def load_fresh_data_and_save_to_disc(symbol_dict, save_path):
    # Extract symbols and names from the symbol_dict
    symbols, names = np.array(sorted(symbol_dict.items())).T
    
    # Initialize an empty DataFrame for storing the prices
    df_crypto = pd.DataFrame()

    # Download and process the price data for each symbol
    for symbol in symbols:
        print(f"Fetching prices for {symbol}...")
        
        # Download the price data using CmcScraper
        scraper = CmcScraper(symbol)
        df_coin_prices = scraper.get_dataframe()

        # Process the price data and add it to df_crypto
        df = pd.DataFrame({
            f"{symbol}_Open": df_coin_prices["Open"],
            f"{symbol}_Close": df_coin_prices["Close"],
            f"{symbol}_Avg": (df_coin_prices["Close"] + df_coin_prices["Open"]) / 2,
            f"{symbol}_p": (df_coin_prices["Open"] - df_coin_prices["Close"]) / df_coin_prices["Open"]
        })
        df_crypto = pd.concat([df_crypto, df], axis=1)

    # Save the price data to a CSV file
    X_df_filtered = df_crypto.filter(like="_p")
    X_df_filtered.to_csv(save_path + "historical_crypto_prices.csv")

    return names, symbols, X_df_filtered
        

# If set to False the data will only be downloaded when you execute the code
# Set to True, if you want a fresh copy of the data.  
fetch_new_data = True 
save_path = '' # path where the price data will be stored in a csv file

# Fetch fresh data via the scraping package, or use data from the csv file on disk
if fetch_new_data == False:
    try:
        print('loading from disk')
        X_df_filtered = pd.read_csv(save_path + 'historical_crypto_prices.csv')
        if 'Unnamed: 0' in X_df_filtered.columns: 
            X_df_filtered = X_df_filtered.drop(['Unnamed: 0'], axis=1)
            symbols, names = np.array(sorted(symbol_dict.items())).T
        print(list(X_df_filtered.columns))
    except:
        print('no existing price data found - loading fresh data from coinmarketcap and saving them to disk')
        names, symbols, X_df_filtered = load_fresh_data_and_save_to_disc(symbol_dict, save_path)
        print(list(symbols))
else:
       print('loading fresh data from coinmarketcap and saving them to disk')
       names, symbols, X_df_filtered = load_fresh_data_and_save_to_disc(symbol_dict, save_path)
       print(list(symbols))

# Limit the price data to the last t days
t= 14 # in days
X_df_filtered = X_df_filtered[:t]
X_df_filtered.head()

	ACM_p		ADA_p		ARK_p		ATM_p		ATOM_p		AVAX_p		BAT_p		BCH_p		BLZ_p		BNB_p		...	THETA_p		UNI_p		USDT_p		VET_p		WAVES_p		XLM_p		XMR_p		XRP_p		ZIL_p		ZRX_p
0	0.031987	-0.037645	-0.005702	0.030928	-0.005897	-0.012404	-0.012262	-0.022529	0.008072	-0.007111	...	-0.021994	-0.023758	-0.000103	-0.021024	-0.015416	-0.004096	-0.022988	-0.027397	-0.016659	-0.012255
1	0.028192	0.065034	0.122306	0.010310	0.093558	0.106811	0.082863	0.075567	0.062105	0.054733	...	0.067264	0.081040	0.000136	0.077203	0.092987	0.078562	0.111519	0.071696	0.076484	0.085094
2	0.040771	0.016097	-0.133345	0.018963	0.011304	-0.033328	-0.007616	0.011458	-0.019993	0.005134	...	-0.005104	-0.024190	0.000077	0.002218	0.008920	0.004139	-0.031822	-0.012107	-0.003906	-0.021170
3	-0.027698	0.005129	-0.031516	-0.002639	0.022235	-0.008117	0.003969	0.019119	0.015403	0.005920	...	0.007992	0.027203	0.000003	0.000701	0.010739	0.005324	-0.007914	0.007168	0.004556	-0.003786
4	-0.021129	-0.019053	0.003273	-0.008121	0.002883	-0.004927	0.002548	-0.000599	0.028492	-0.012181	...	0.000198	-0.025817	-0.000047	-0.002800	-0.051515	-0.004861	0.015134	-0.000596	-0.010343	0.004530

The data looks good, so let’s continue.

Step #2 Plotting Crypto Price Charts

Now that the data is available, we can visualize it in various line graphs. The visualization helps us better understand what kind of data we are dealing with and check if the download was successful.

# Create Prices Charts for all Cryptocurrencies
list_length = X_df_filtered.shape[1]
ncols = 10
nrows = int(round(list_length / ncols, 0))
height = list_length/3 if list_length > 30 else 4
fig, axs = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, sharey=True, figsize=(20, height))
for i, ax in enumerate(fig.axes):
        if i < list_length:
            sns.lineplot(data=X_df_filtered, x=X_df_filtered.index, y=X_df_filtered.iloc[:, i], ax=ax)
            ax.set_title(X_df_filtered.columns[i])
plt.show()

We can see the lineplots for all cryptocurrencies and everything looks as expected.

Step #3 Clustering Cryptocurrencies using Affinity Propagation

Next, we must prepare the data and run the affinity propagation algorithm. For some cryptocurrencies, we may encounter data that contains NaN values. Because clustering is sensitive to missing values, we must ensure good data quality. In addition, the Python code below will convert the DataFrame into a NumPy array and transpose it into a form where we have crypto assets as records and the days as columns.

Running the code below returns a dictionary of clusters with the cryptocurrencies assigned to them by the affinity propagation algorithm.

# Drop NaN values
X_df = pd.DataFrame(np.array(X_df_filtered)).dropna()
# Transpose the data to structure prices along columns
X = X_df.copy()
X /= X.std(axis=0)
X = np.array(X)
# Define an edge model based on covariance
edge_model = covariance.GraphicalLassoCV()
# Standardize the time series
edge_model.fit(X)
# Group cryptos to clusters using affinity propagation
# The number of clusters will be determined by the algorithm
cluster_centers_indices , labels = cluster.affinity_propagation(edge_model.covariance_, random_state=1)
cluster_dict = {}
n_labels = labels.max()
print(f"{n_labels} Clusters")
for i in range(n_labels + 1):
    clusters = ', '.join(names[labels == i])
    print('Cluster %i: %s' % ((i + 1), clusters))
    cluster_dict[i] = (clusters)

9 Clusters
Cluster 1: Binance Coin, Cake Defi
Cluster 2: Bitcoin Cash, Bitcoin, BitTorrent, Decred, EOS, Ethereum Classic, Ethereum, Ampleforth, Komodo, Solana, Sys Coin, DOT
Cluster 3: Celsius
Cluster 4: Doge Coin
Cluster 5: Cardano, ATOM, Avalance, Enjin, Internet Computer, Link, Loopring, Polygon, IOTA, NEO, Synthetix, Theta, Vechain
Cluster 6: Litecoin
Cluster 7: ACM Token, Atletico Madrid Token, Chilliz, Juventus Turin Token, PSG Token
Cluster 8: LRC
Cluster 9: Tether
Cluster 10: ARK, Battoken, BLZ, Digibyte, AS Rom Token, WAVES, Stellar Lumen, Monero, Ripple, Zilliqa, Zer0

We can see that the algorithm has identified 13 different clusters in the data and a couple of clusters with only a single member. You will most likely encounter different results depending on when you run it.

Step #4 Create a 2D Positioning Model based on the Graph Structure

In addition to clusters, we want to show the covariance between cryptocurrencies in our Crypto Market map. We need a graph-like structure that contains the covariance and position data of the cryptocurrencies for each crypto pair.

In addition, we use a node position model that calculates their relative position on a 2D plane from the covariance of the cryptocurrencies. However, the positions are only relative, so the absolute axes have no meaning.

# Create a node_position_model that find the best position of the cryptos on a 2D plane
# The number of components defines the dimensions in which the nodes will be positioned
node_position_model = manifold.LocallyLinearEmbedding(n_components=2, eigen_solver='dense', n_neighbors=20)
embedding = node_position_model.fit_transform(X.T).T
# The result are x and y coordindates for all cryptocurrencies
pd.DataFrame(embedding)
# Create an edge_model that represents the partial correlations between the nodes
partial_correlations = edge_model.precision_.copy()
d = 1 / np.sqrt(np.diag(partial_correlations))
partial_correlations *= d
partial_correlations *= d[:, np.newaxis]
# Only consider partial correlations above a specific threshold (0.02)
non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)
# Convert the Positioning Model into a DataFrame
data = pd.DataFrame.from_dict({"embedding_x":embedding[0],"embedding_y":embedding[1]})
# Add the labels to the 2D positioning model
data["labels"] = labels
print(data.shape)
data.head()

(48, 3)
	embedding_x	embedding_y	labels
0	0.400590	-0.136473	6
1	-0.081908	-0.086039	4
2	-0.033982	-0.038526	9
3	0.416745	0.076849	6
4	-0.041938	0.031966	4

The next step is to create a graph of the partial correlations.

Step #5 Visualize the Crypto Market Structure

Our goal is to visualize differences in the covariance between crypto pairs by varying the connection strengths. We calculate the line strength by normalizing the covariance of the crypto pairs. In addition, we visualize the distribution of the covariance.

# Create an array with the segments for connecting the data points
start_idx, end_idx = np.where(non_zero) 
segments = [[np.array([embedding[:, start], embedding[:, stop]]).T, start, stop] for start, stop in zip(start_idx, end_idx)]
# Create a normalized representation of partial correlation between crypto currencies
# We can later use covariance to vizualize the strength of the connections
pc = np.abs(partial_correlations[non_zero])
normalized = (pc-min(pc))/(max(pc)-min(pc))
# plot the distribution of covariance between the cryptocurrencies
sns.histplot(pc)

The hist plot shows that the covariance between the crypto pairs is mostly below 0.005.

Finally, it is time to map cryptocurrencies on a 2D plane. To do this, we first define the cryptocurrencies using their relative position data with a scatterplot. We set the color of the points based on their clusters so that points in the same cluster are colored the same. Subsequently, we connect the points to the data from the edge model. The covariance between the crypto pairs determines the strength of their connections.

We also define the color of the connections as follows.

The map only shows connections with a covariance greater than 0.002.
Connections with a covariance greater than 0.05 are colored red.
Otherwise, connections between points within a cluster are shown in the cluster’s color.
We color connections in grey that are between points of different clusters.

Last but not least, we add the labels of the cryptocurrencies.

# Visualization
plt.figure(1, facecolor='w', figsize=(20, 8))
plt.clf()
ax = plt.axes([0., 0., 1., 1.])

# Plot the nodes using the coordinates of our embedding
sc = sns.scatterplot(
    data=data,
    x="embedding_x",
    y="embedding_y",
    zorder=1,
    s=350 * d ** 2,
    c=labels,
    cmap=plt.cm.nipy_spectral,
    alpha=.9,
    #palette="muted",
)

# Plot the covariance edges between the nodes (scatter points)
line_strength = 3.2
    
for index, ((x, y), start, stop) in enumerate(segments):     
    norm_partial_correlation = normalized[index]
    if list(data.iloc[[start]]['labels'])[0] == list(data.iloc[[stop]]['labels'])[0]:
        if norm_partial_correlation > 0.5:
            color = 'red'; linestyle='solid'
        else:
            color = plt.cm.nipy_spectral(list(data.iloc[[start]]['labels'])[0] / float(n_labels)); linestyle='solid'
    else:
        if norm_partial_correlation > 0.5:
            color = 'red'; linestyle='solid'
        else:
            color = 'grey'; linestyle='dashed'
    # Plot the edges
    # if x and y larger than 0
    if x[0] > 0 and y[0] > 0:
        plt.plot(x, y, alpha=.4, zorder=0, linewidth=normalized[index]*line_strength, color=color, linestyle=linestyle)

    
# Labels the nodes and position the labels to avoid overlap with other labels
for name, label, (x, y) in zip(names, labels, embedding.T):
    color = plt.cm.nipy_spectral(label / float(n_labels))
    ax.annotate(
        name,
        xy=(x, y),
        xytext=(5, 2),
        textcoords='offset points',
        ha='right',
        va='bottom',
        fontsize=10,
        color='black',
        bbox=dict(facecolor='w', edgecolor="w", alpha=.0),
     )

Note that you will likely see a different map when you run the code on your machine. Differences result from changes in market prices and covariance that lead to other graph structures.

Let’s see what the crypto market map tells us.

Interpreting the Cryptomarket Map

The 2D crypto market map tells us several things:

Most cryptos fall into the light green and dark green clusters corresponding to different types of crypto (Decentralized Finance Coins, NFT/Metaverse Coins).
There is a significant covariance between large-cap players in the crypto space, such as Cardano and Loopring and Ethereum and Bitcoin, which is plausible considering recent price movements. Some results are surprising, for example, the partial correlation between NEO and Ethereum Classic.
Some clusters are isolated and contain only a single member, for example, Tether, Komodo, AC Milan token, Wave token, and Dogecoin). The reason is that the prices of these coins/tokens have developed independently of the market.
- Tether is a stablecoin that does not change in price. It, therefore, strongly differs from the other cryptocurrencies on our map.
- Komodo has been trading sideways without following the general market trend.
- And the MCM token is a soccer token that has recently outperformed the market.
Soccer tokens are colored in dark blue. These tokens’ prices correlate with how the soccer clubs performed during the current season. It, therefore, makes perfect sense that these tokens are grouped into a cluster. An exception is the AC Milan token, which recently performed better than the other soccer tokens.

Step #6 Creating a 3D Representation

Instead of a 2D representation of the data points, we can also use a 3D node positioning model. For this purpose, the node positioning model distributes the affinity values over three dimensions.

# Find the best position of the cryptos on a 3D plane
node_position_model = manifold.LocallyLinearEmbedding(n_components=3, eigen_solver='dense', n_neighbors=20)
embedding = node_position_model.fit_transform(X.T).T
# The result are x and y coordindates for all cryptocurrencies
pd.DataFrame(embedding)
# Display a graph of the partial correlations
partial_correlations = edge_model.precision_.copy()
d = 1 / np.sqrt(np.diag(partial_correlations))
partial_correlations *= d
partial_correlations *= d[:, np.newaxis]
non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)
data = pd.DataFrame.from_dict({"embedding_x":embedding[0],"embedding_y":embedding[1],"embedding_z":embedding[1]})
data["labels"] = labels
data["names"] = names
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot(projection='3d')
xs = data["embedding_x"]
ys = data["embedding_y"]
zs = data["embedding_z"]
sc = ax.scatter(xs, ys, zs, c=labels, s=100)
    
for i in range(len(data)):
    x = xs[i]
    y = ys[i]
    z = zs[i]
    label = data["names"][i]
    ax.text(x, y, z, label)
    
plt.legend(*sc.legend_elements(), bbox_to_anchor=(1.05, 1), loc=2)
plt.show()

Summary

Affinity propagation is a powerful technique for clustering items when the optimal number of clusters is unknown. In this article, we’ve demonstrated how to apply affinity propagation to analyze the cryptocurrency market and identify groups of assets based on similar price fluctuations.

In our example, we identified 13 groups of cryptocurrencies without specifying the number of clusters in advance. We also visualized the market structure on a 2D and 3D map using a node distribution technique. This approach can be extended to analyze and cluster stock markets, highlighting complex price patterns among multiple financial assets.

Once you’ve identified clusters, you can dive deeper into individual groups. Sometimes, outliers that temporarily break out of their usual pattern indicate interesting investment opportunities. These outliers can eventually return to the price pattern of their group, or they may represent forerunners of their group, indicating broader market movements.

By using affinity propagation, we can visualize financial assets in a new and exciting way. If you have any questions or comments about this approach, please let me know.

Sources and Further Reading

This article modifies some of the code from Scikit-learn and adapts it from the stock market to cryptocurrencies.

Jansen (2020) Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python
Aurélien Géron (2019) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
David Forsyth (2019) Applied Machine Learning Springer
Andriy Burkov (2020) Machine Learning Engineering
Images are created using Midjourney, an AI that creates images from text.

The links above to Amazon are affiliate links. By buying through these links, you support the Relataly.com blog and help to cover the hosting costs. Using the links does not affect the price.

The post Unveiling Hidden Patterns in the Cryptocurrency Market with Affinity Propagation and Python appeared first on relataly.com.

Stock Market Forecasting Neural Networks for Multi-Output Regression in Python

Florian Follonier — Tue, 13 Jul 2021 21:10:23 +0000

Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and thus deliver a more detailed projection of how a time series could develop in the future. This article is a hands-on Python tutorial that shows how to design a neural network architecture with multiple outputs. The goal is to create a multi-output model for stock-price forecasting using Python and Keras. By the end of this tutorial, you will have learned how to design a multi-output model for stock price forecasting using Python and Keras. This knowledge can be applied to other types of time series forecasting tasks, such as weather forecasting or sales forecasting.

This article proceeds as follows: We briefly discuss the architecture of a multi-output neural network. After familiarizing ourselves with the model architecture, we develop a Keras neural network for multi-output regression. For data preparation, we perform various steps, including cleaning, splitting, selecting, and scaling the data. Afterward, we define a model architecture with multiple LSTM layers and ten output neurons in the last layer. This architecture enables the model to generate projections for ten consecutive steps. After configuring the model architecture, we train the model with the historical daily prices of the Apple stock. Finally, we use this model to generate a ten-day forecast.

Disclaimer

Multi-Output Regression vs. Single-Output Regression

In time series regression, we train a statistical model on the past values of a time series to make statements about how the time series develops further. During model training, we feed the model with so-called mini-batches and the corresponding target values. The model then creates forecasts for all input batches and compares these predictions to the actual target values to calculate the residuals (prediction errors). In this way, the model can adjust its parameters iteratively and learn to make better predictions.

Multivariate forecasting models take into account multiple input variables, such as historical time series data and additional features like moving averages or momentum indicators, to improve the accuracy of their predictions. The idea is that these various variables can help the model identify patterns in the data that suggest future price movements.

An exemplary architecture of a neural network with five input neurons (blue) and four output neurons (red)

The Architecture of a Neural Network with Multiple Outputs

Next, we will discuss the architecture of a neural network with multiple outputs. The architecture consists of several layers, including an input layer, several hidden layers, and an output layer. The number of neurons in the first layer must match the input data, and the number of neurons in the output layer determines the period length of the predictions.

Models with a single neuron in the output layer are used to predict a single time step. It is possible to predict multiple price steps with a single-output model. It requires a rolling forecasting approach in which the outputs are iteratively reused to make further-reaching predictions. However, this way is somewhat cumbersome. A more elegant way is to train a multi-output model right away.

The inputs and outputs of a neural network for time series regression with five input neurons and four outputs

Training Neural Networks with Multiple Outputs

A model with multiple neurons in the output layer can predict numerous steps once per batch. Multi-output regression models train on many sequences of subsequent values, followed by the consecutive output sequence. The model architecture thus contains multiple neurons in the initial layer and various neurons in the output layer (as illustrated).

In a multi-output regression model, each neuron in the output layer is responsible for predicting a different time step in the future. To train such a model, you need to provide a sequence of input data followed by the corresponding sequence of output data. For example, if you want to predict the stock price for the next ten days, you would provide a sequence of input data containing the historical stock prices for the past 50 days, followed by a sequence of output data containing the stock prices for the next 10 days.

The model will then learn to map the input sequence to the output sequence so that it can make predictions for multiple time steps in the future based on the input data.

In the next part of this tutorial, we will walk through the process of developing a multi-output regression model in more detail.

Implementing a Neural Network Model for Multi-Output Multi-Step Regression in Python

Let’s get started with the hands-on Python part. In the following, we will develop a neural network with Keras and Tensorflow that forecasts the Apple stock price. To prepare the data for a neural network with multiple outputs in time series forecasting, we will spend the most time preparing it and bringing it into the right shape. Broadly this involves the following steps:

Load the time series data that we want to use as input and output for your model. We use historical price data that is available via the yahoo finance API.
Then we split our data into training and testing sets. We will use the training set to fit the model and the testing set to evaluate the model’s performance.
Preprocess the data: This includes scaling the data and selecting relevant features.
Reshape the data and bring them into a format that can be input into the neural network. This involves converting the data into a 3D array for time series data.
Finally, we will train our model and generate the forecasting.

The code is available on the GitHub repository.

View on GitHub Relataly GitHub Repo

Neural network architectures with multiple outputs allow for more potent solutions but are more complex to train. Image created with Midjourney.

Prerequisites

Before beginning the coding part, ensure that you have set up your Python 3 environment and required packages. If you don’t have a Python environment, consider Anaconda. To set it up, you can follow the steps in this tutorial.

Also, make sure you install all required packages. In this tutorial, we will be working with the following standard packages:

In addition, we will be using the machine learning libraries Keras, Scikit-learn, and Tensorflow. For visualization, we will be using the Seaborn package.

Please also have either the pandas_datareader or the yfinance package installed. You will use one of these packages to retrieve the historical stock quotes.

You can install these packages using console commands:

pip install
conda install (if you are using the anaconda packet manager)

Step #1: Load the Data

The Pandas DataReader library is our first choice for interacting with the yahoo finance API. If the library causes a problem (it sometimes does), you can also use the yfinance package, which should return the same data. We begin by loading historical price quotes of the Apple stock from the public yahoo finance API. Running the code below will load the data into a Pandas DataFrame.

# import pandas_datareader as webreader # Remote data access for pandas
import math # Mathematical functions 
import numpy as np # Fundamental package for scientific computing with Python
import pandas as pd # Additional functions for analysing and manipulating data
from datetime import date, timedelta, datetime # Date Functions
from pandas.plotting import register_matplotlib_converters # This function adds plotting functions for calender dates
import matplotlib.pyplot as plt # Important package for visualization - we use this to plot the market data
import matplotlib.dates as mdates # Formatting dates
from sklearn.metrics import mean_absolute_error, mean_squared_error # Packages for measuring model performance / errors
from keras.models import Sequential # Deep learning library, used for neural networks
from keras.layers import LSTM, Dense, Dropout # Deep learning classes for recurrent and regular densely-connected layers
from keras.callbacks import EarlyStopping # EarlyStopping during model training
from sklearn.preprocessing import RobustScaler, MinMaxScaler # This Scaler removes the median and scales the data according to the quantile range to normalize the price data 
import seaborn as sns

# from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
# symbols = get_nasdaq_symbols()

# Setting the timeframe for the data extraction
today = date.today()
date_today = today.strftime("%Y-%m-%d")
date_start = '2010-01-01'

# Getting NASDAQ quotes
stockname = 'Apple'
symbol = 'AAPL'
# df = webreader.DataReader(
#     symbol, start=date_start, end=date_today, data_source="yahoo"
# )

import yfinance as yf #Alternative package if webreader does not work: pip install yfinance
df = yf.download(symbol, start=date_start, end=date_today)

# # Create a quick overview of the dataset
df.head()

Tensorflow Version: 2.6.0
Num GPUs: 1
[*********************100%***********************]  1 of 1 completed
			Open		High		Low			Close		Adj Close	Volume
Date						
2010-01-04	7.622500	7.660714	7.585000	7.643214	6.515213	493729600
2010-01-05	7.664286	7.699643	7.616071	7.656429	6.526477	601904800
2010-01-06	7.656429	7.686786	7.526786	7.534643	6.422666	552160000
2010-01-07	7.562500	7.571429	7.466071	7.520714	6.410791	477131200
2010-01-08	7.510714	7.571429	7.466429	7.570714	6.453413	447610800

The data should comprise the following columns:

Close
Open
High
Low
Adj Close
Volume

The target variable that we are trying to predict is the Closing price (Close).

Step #2: Explore the Data

Once we have loaded the data, we print a quick overview of the time-series data using different line graphs. The following code will plot a line chart for each column in df_plot using the seaborn library. The charts will be organized in a grid with nrows number of rows and ncols number of columns. The sharex parameter is set to True, which means that the x-axes of the subplots will be shared. The figsize parameter determines the size of the plot in inches.

# Plot line charts
df_plot = df.copy()

ncols = 2
nrows = int(round(df_plot.shape[1] / ncols, 0))

fig, ax = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, figsize=(14, 7))
for i, ax in enumerate(fig.axes):
        sns.lineplot(data = df_plot.iloc[:, i], ax=ax)
        ax.tick_params(axis="x", rotation=30, labelsize=10, length=0)
        ax.xaxis.set_major_locator(mdates.AutoDateLocator())
fig.tight_layout()
plt.show()

The line plots look as expected and reflect the Apple stock price history. Because we are fetching daily data from an API, please note that the lineplots will look different depending on when you run the code.

Step #3: Preprocess the Data

Next, we prepare the data for the training process of our multi-output forecasting model. Preparing the data for multivariate forecasting involves several steps:

Selecting features for model training
Scaling and splitting the data into separate sets for training and testing
Slicing the time series into several shifted training batches

Remember that the steps are specific to our data and the use case. The steps required to prepare the data for a neural network with multiple outputs in time series forecasting will depend on the characteristics of your data and the requirements of your model. It is essential to consider these factors and tailor your data preparation accordingly and carefully.

3.1 Basic Preparations

We begin by creating a copy of the initial data and resetting the index.

# Indexing Batches
df_train = df.sort_values(by=['Date']).copy()

# We safe a copy of the dates index, before we need to reset it to numbers
date_index = df_train.index

# We reset the index, so we can convert the date-index to a number-index
df_train = df_train.reset_index(drop=True).copy()
df_train.head(5)

			Open		High		Low			Close		Adj Close	Volume
Date						
2022-11-29	144.289993	144.809998	140.350006	141.169998	141.169998	83763800
2022-11-30	141.399994	148.720001	140.550003	148.029999	148.029999	111224400
2022-12-01	148.210007	149.130005	146.610001	148.309998	148.309998	71250400
2022-12-02	145.960007	148.000000	145.649994	147.809998	147.809998	65421400
2022-12-05	147.770004	150.919998	145.770004	146.630005	146.630005	68732400

3.2 Feature Selection and Scaling

We proceed with feature selection. To keep things simple, we will use the features from the input data without any modifications. After selecting the features, we scale them to a range between 0 and 1. To ease unscaling the predictions after training, we create two different scalers: One for the training data, which takes five columns, and one for the output data that scales a single column (the Close Price). I have covered feature engineering in a separate article if you want to learn more about this topic.

def prepare_data(df):

    # List of considered Features
    FEATURES = ['Open', 'High', 'Low', 'Close', 'Volume']

    print('FEATURE LIST')
    print([f for f in FEATURES])

    # Create the dataset with features and filter the data to the list of FEATURES
    df_filter = df[FEATURES]
    
    # Convert the data to numpy values
    np_filter_unscaled = np.array(df_filter)
    #np_filter_unscaled = np.reshape(np_unscaled, (df_filter.shape[0], -1))
    print(np_filter_unscaled.shape)

    np_c_unscaled = np.array(df['Close']).reshape(-1, 1)
    
    return np_filter_unscaled, np_c_unscaled, df_filter
    
np_filter_unscaled, np_c_unscaled, df_filter = prepare_data(df_train)
                                          
# Creating a separate scaler that works on a single column for scaling predictions
# Scale each feature to a range between 0 and 1
scaler_train = MinMaxScaler()
np_scaled = scaler_train.fit_transform(np_filter_unscaled)
    
# Create a separate scaler for a single column
scaler_pred = MinMaxScaler()
np_scaled_c = scaler_pred.fit_transform(np_c_unscaled)

FEATURE LIST
['Open', 'High', 'Low', 'Close', 'Volume']
(3254, 5)

The final step of the data preparation is to create the structure for the input data. This structure needs to match the input layer of the model architecture.

3.3 Slicing the Data for a Model with Multiple In- and Outputs

The code below starts a sliding window process that cuts the initial time series data into multiple slices, i.e., mini-batches. Each batch is a smaller fraction of the initial time series shifted by a single step. Because we will feed our model with multivariate input data, the time series consists of five input columns/features. Each batch comprises a period of 50 steps from the time series and an output sequence of ten consecutive values. To validate that the batches have the right shape, we visualize mini-batches in a line graph with their consecutive target values.

# Set the input_sequence_length length - this is the timeframe used to make a single prediction
input_sequence_length = 50
# The output sequence length is the number of steps that the neural network predicts
output_sequence_length = 10 #

# Prediction Index
index_Close = df_train.columns.get_loc("Close")

# Split the training data into train and train data sets
# As a first step, we get the number of rows to train the model on 80% of the data 
train_data_length = math.ceil(np_scaled.shape[0] * 0.8)

# Create the training and test data
train_data = np_scaled[:train_data_length, :]
test_data = np_scaled[train_data_length - input_sequence_length:, :]

# The RNN needs data with the format of [samples, time steps, features]
# Here, we create N samples, input_sequence_length time steps per sample, and f features
def partition_dataset(input_sequence_length, output_sequence_length, data):
    x, y = [], []
    data_len = data.shape[0]
    for i in range(input_sequence_length, data_len - output_sequence_length):
        x.append(data[i-input_sequence_length:i,:]) #contains input_sequence_length values 0-input_sequence_length * columns
        y.append(data[i:i + output_sequence_length, index_Close]) #contains the prediction values for validation (3rd column = Close),  for single-step prediction
    
    # Convert the x and y to numpy arrays
    x = np.array(x)
    y = np.array(y)
    return x, y

# Generate training data and test data
x_train, y_train = partition_dataset(input_sequence_length, output_sequence_length, train_data)
x_test, y_test = partition_dataset(input_sequence_length, output_sequence_length, test_data)

# Print the shapes: the result is: (rows, training_sequence, features) (prediction value, )
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)

# Validate that the prediction value and the input match up
# The last close price of the second input sample should equal the first prediction value
nrows = 3 # number of shifted plots
fig, ax = plt.subplots(nrows=nrows, ncols=1, figsize=(16, 8))
for i, ax in enumerate(fig.axes):
    xtrain = pd.DataFrame(x_train[i][:,index_Close], columns={f'x_train_{i}'})
    ytrain = pd.DataFrame(y_train[i][:output_sequence_length-1], columns={f'y_train_{i}'})
    ytrain.index = np.arange(input_sequence_length, input_sequence_length + output_sequence_length-1)
    xtrain_ = pd.concat([xtrain, ytrain[:1].rename(columns={ytrain.columns[0]:xtrain.columns[0]})])
    df_merge = pd.concat([xtrain_, ytrain])
    sns.lineplot(data = df_merge, ax=ax)
plt.show

(2544, 50, 5) (2544, 10)
(640, 50, 5) (640, 10)

Step #4: Prepare the Neural Network Architecture and Train the Multi-Output Regression Model

Now that we have the training data prepared and ready, the next step is to configure the architecture of the multi-out neural network. Because we will be using multiple input series, our model is, in fact, a multivariate architecture so that it corresponds to the input training batches.

4.1 Configuring and Training the Model

We choose a comparably simple architecture with only two LSTM layers and two additional dense layers. The first dense layer has 20 neurons, and the second layer is the output layer, which has ten output neurons. If you wonder how I got to the number of neurons in the third layer, I conducted several experiments and found that this number leads to solid results.

To ensure that the architecture matches our input data’s structure, we reuse the variables for the previous code section (n_input_neurons, n_output_neurons. The input sequence length is 50, and the output sequence (the steps for the period we want to predict) is ten.

# Configure the neural network model
model = Sequential()
n_output_neurons = output_sequence_length

# Model with n_neurons = inputshape Timestamps, each with x_train.shape[2] variables
n_input_neurons = x_train.shape[1] * x_train.shape[2]
print(n_input_neurons, x_train.shape[1], x_train.shape[2])
model.add(LSTM(n_input_neurons, return_sequences=True, input_shape=(x_train.shape[1], x_train.shape[2]))) 
model.add(LSTM(n_input_neurons, return_sequences=False))
model.add(Dense(20))
model.add(Dense(n_output_neurons))

# Compile the model
model.compile(optimizer='adam', loss='mse')

After configuring the model architecture, we can initiate the training process and illustrate how the loss develops over the training epochs.

# Training the model
epochs = 10
batch_size = 16
early_stop = EarlyStopping(monitor='loss', patience=5, verbose=1)
history = model.fit(x_train, y_train, 
                    batch_size=batch_size, 
                    epochs=epochs,
                    validation_data=(x_test, y_test)
                   )
                    
                    #callbacks=[early_stop])

Epoch 1/5
159/159 [==============================] - 7s 14ms/step - loss: 0.0047 - val_loss: 0.0262
Epoch 2/5
159/159 [==============================] - 2s 11ms/step - loss: 3.6759e-04 - val_loss: 0.0097
Epoch 3/5
159/159 [==============================] - 2s 11ms/step - loss: 1.5222e-04 - val_loss: 0.0056
Epoch 4/5
159/159 [==============================] - 2s 11ms/step - loss: 1.0327e-04 - val_loss: 0.0031
Epoch 5/5
159/159 [==============================] - 2s 11ms/step - loss: 1.1690e-04 - val_loss: 0.0026

4.2 Loss Curve

Next, we plot the loss curve, which represents the amount of error between the model’s predicted values and the actual values in the training data. A lower loss value indicates that the model makes more accurate predictions on the training data.

# Plot training & validation loss values
fig, ax = plt.subplots(figsize=(10, 5), sharex=True)
plt.plot(history.history["loss"])
plt.title("Model loss")
plt.ylabel("Loss")
plt.xlabel("Epoch")
ax.xaxis.set_major_locator(plt.MaxNLocator(epochs))
plt.legend(["Train", "Test"], loc="upper left")
plt.grid()
plt.show()

As we can see, the loss curve drops quickly during training, which typically means that the model is quickly learning to make accurate predictions.

Step #5 Evaluate Model Performance

Now that we have trained the model, we can make forecasts on the test data and use traditional regression metrics such as the MAE, MAPE, or MDAPE to measure the performance of our model.

# Get the predicted values
y_pred_scaled = model.predict(x_test)

# Unscale the predicted values
y_pred = scaler_pred.inverse_transform(y_pred_scaled)
y_test_unscaled = scaler_pred.inverse_transform(y_test).reshape(-1, output_sequence_length)

# Mean Absolute Error (MAE)
MAE = mean_absolute_error(y_test_unscaled, y_pred)
print(f'Median Absolute Error (MAE): {np.round(MAE, 2)}')

# Mean Absolute Percentage Error (MAPE)
MAPE = np.mean((np.abs(np.subtract(y_test_unscaled, y_pred)/ y_test_unscaled))) * 100
print(f'Mean Absolute Percentage Error (MAPE): {np.round(MAPE, 2)} %')

# Median Absolute Percentage Error (MDAPE)
MDAPE = np.median((np.abs(np.subtract(y_test_unscaled, y_pred)/ y_test_unscaled)) ) * 100
print(f'Median Absolute Percentage Error (MDAPE): {np.round(MDAPE, 2)} %')


def prepare_df(i, x, y, y_pred_unscaled):
    # Undo the scaling on x, reshape the testset into a one-dimensional array, so that it fits to the pred scaler
    x_test_unscaled_df = pd.DataFrame(scaler_pred.inverse_transform((x[i]))[:,index_Close]).rename(columns={0:'x_test'})
    
    y_test_unscaled_df = []
    # Undo the scaling on y
    if type(y) == np.ndarray:
        y_test_unscaled_df = pd.DataFrame(scaler_pred.inverse_transform(y)[i]).rename(columns={0:'y_test'})

    # Create a dataframe for the y_pred at position i, y_pred is already unscaled
    y_pred_df = pd.DataFrame(y_pred_unscaled[i]).rename(columns={0:'y_pred'})
    return x_test_unscaled_df, y_pred_df, y_test_unscaled_df


def plot_multi_test_forecast(x_test_unscaled_df, y_test_unscaled_df, y_pred_df, title): 
    # Package y_pred_unscaled and y_test_unscaled into a dataframe with columns pred and true   
    if type(y_test_unscaled_df) == pd.core.frame.DataFrame:
        df_merge = y_pred_df.join(y_test_unscaled_df, how='left')
    else:
        df_merge = y_pred_df.copy()
    
    # Merge the dataframes 
    df_merge_ = pd.concat([x_test_unscaled_df, df_merge]).reset_index(drop=True)
    
    # Plot the linecharts
    fig, ax = plt.subplots(figsize=(20, 8))
    plt.title(title, fontsize=12)
    ax.set(ylabel = stockname + "_stock_price_quotes")
    sns.lineplot(data = df_merge_, linewidth=2.0, ax=ax)

# Creates a linechart for a specific test batch_number and corresponding test predictions
batch_number = 50
x_test_unscaled_df, y_pred_df, y_test_unscaled_df = prepare_df(i, x_test, y_test, y_pred)
title = f"Predictions vs y_test - test batch number {batch_number}"
plot_multi_test_forecast(x_test_unscaled_df, y_test_unscaled_df, y_pred_df, title)

The quality of the predictions is acceptable, considering that this tutorial aimed not to achieve excellent predictions but to demonstrate the process and architecture of training a multi-output regression. So, there is certainly room for improvement. Feel free to experiment with different features or try other hyperparameters and neural network layers.

Step #6 Create a New Forecast

Finally, let’s create a forecast on a new dataset. We take the scaled dataset from section 2 (np_scaled) and extract a series with the latest 50 values. The data is reshaped into a 3D array with shape (1, 50, 5) to match the expected input shape of the model. We use these values to generate a new prediction for the next ten days using the predict method. We store the result in the y_pred_scaled variable. In addition, we need to transform the predictions back to the original scale. We do this by using the inverse_transform method of the scaler_pred object, which was fit on the training data. Finally, we visualize the multi-step forecast in another line chart.

# Get the latest input batch from the test dataset, which is contains the price values for the last ten trading days
x_test_latest_batch = np_scaled[-50:,:].reshape(1,50,5)

# Predict on the batch
y_pred_scaled = model.predict(x_test_latest_batch)
y_pred_unscaled = scaler_pred.inverse_transform(y_pred_scaled)

# Prepare the data and plot the input data and the predictions
x_test_unscaled_df, y_test_unscaled_df, _ = prepare_df(0, x_test_latest_batch, '', y_pred_unscaled)
plot_multi_test_forecast(x_test_unscaled_df, '', y_test_unscaled_df, "x_new Vs. y_new_pred")

Summary

In this tutorial, we demonstrated how to use multiple output neural networks to make predictions at different time steps. We first discussed the architecture of a recurrent neural network and how it can be used to process sequential data. We then showed how to properly preprocess the data and split it into training and test sets for training a multi-output regression model.

Next, we trained a model to predict the stock price of Apple ten steps into the future using historical data. We also discussed how to use the trained model to make multi-step predictions on new data and how to visualize the results.

To further improve the performance of the model, you can experiment with different hyperparameters and adjust the model architecture. For example, adding more neurons to the output layers will increase the prediction horizon, but remember that prediction error will also increase as the horizon lengthens. You can also try using different activation functions or adding more layers to the model to see how it affects the performance.

I hope this article was helpful in understanding multi-output neural networks better. If you have any questions or comments, please let me know.

Sources and Further Reading

The links above to Amazon are affiliate links. By buying through these links, you support the Relataly.com blog and help to cover the hosting costs. Using the links does not affect the price.

If you want to learn about an alternative approach to univariate stock market forecasting, consider taking a look at Facebook Prophet or ARIMA models

The post Stock Market Forecasting Neural Networks for Multi-Output Regression in Python appeared first on relataly.com.

Automate Crypto Trading with a Python-Powered Twitter Bot and Gate.io Signals

Florian Follonier — Wed, 19 May 2021 04:57:00 +0000

This tutorial develops a Twitter bot in Python that will generate automated trading signals. The bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange Gate.io and analyze it using predefined rules. Whenever the bot detects a relevant price change, it automatically posts a tweet via Twitter. Simple Twitter bots can proactively inform their audiences about relevant events in the market. Such an event can be a sharp rise or fall in price or a sudden spike in the trading volume. If we examine data for specific price movements, we can also store these events and use them later to train a predictive model.

More advanced signal bots use predictive models to signal when it is appropriate to enter or exit the market. Or the bot executes the buy- and sell-orders directly itself. A well-defined signaling logic can therefore constitute the first step toward algorithmic trading. But one thing at a time. So in this article, we will begin by developing a simple signal bot.

The rest of this article is structured as follows. First, we take a look at the different code modules of the Twitter bot. After that, we’ll implement the other code modules in Python. Finally, we will integrate the modules and run some tests. We will also quickly introduce the APIs used to build the bot.

Bots can do a lot of cool things but should be used with caution. Image created with Midjourney

Different Modules of the Signal Bot

This section briefly describes the conceptual architecture of the Crypto Twitter bot. Its architecture adheres to a modular design pattern and separates into four loosely coupled modules. Each module has a clear function.

The Data Collection Module retrieves price data from the crypto exchange Gate.io. The module sends requests at regular intervals against the gate.io API. The module adds the data to separate data stores – one for each cryptocurrency. It then forwards the data to the preprocessing module.
The Data Preprocessing Module calculates the statistical indicators, such as moving averages or means, which become the basis for the signaling logic.
The Signaling Module searches for relevant events based on the indicator values provided. If a relevant event is detected, it is reported to the communication module.
The Communication Module connects to the Twitter API. As soon as it is informed about a new event, it tweets about this event on Twitter.

Now that you are familiar with the modules of our Crypto Twitter Bot, we can take a look at its underlying APIs.

Components of the Relataly Crypto Signal Bot

About the APIs Used in this Tutorial

In this tutorial, we will be using two APIs:

The Gate.io API to fetch price data.
Twitter to post Tweets about Trading Signals

The Gate.io API

Firstly, we will be using the Gate.io API to obtain prices for various cryptocurrencies. Gate.io is one of the smaller crypto exchanges in the crypto-verse. However, it offers a wide range of smaller cryptocurrencies, especially those you cannot trade anywhere else. As of now, the gate.io market endpoint does not require authentication to use its essential functions.

Check out our recent relataly gate.io tutorial to learn how to pull data via the gate.io API in Python.

The Twitter API

The second API that our bot will use is the Twitter API. We will use this API via the Python package Tweepy to post crypto price signals. Check out this article if you are looking for a simple code example of submitting tweets via the Twitter API. If you don’t want to use Twitter, you can disable its use in the code.

Posting tweets via the API requires authentication with a valid developer account. You can apply for a developer account for free on the Twitter developer website. Just be aware that the confirmation can sometimes take several days.

Storing the Twitter API Key

Storing API keys in your code can compromise the security of your application. If the code is made public, for example, by publishing it on a code-sharing website like GitHub, anyone who has access to the code can use the API key to make requests to the API and potentially access sensitive information or cause harm to your account or application. A better practice is to import and access the API key from a separate YAML file, from where you can import it into your project. To store the Twitter API Key, create a YAML file with the name “api_config_twitter.yml” and insert your API key into this file as follows:

api_key: “your api key”

Implementing a Twitter Signal Bot using Python

In this article, we will walk through the process of creating a Twitter bot that automatically tweets updates about cryptocurrency prices. The bot will be designed to pull real-time data on cryptocurrency prices from an external API, and then automatically generate and post tweets on a regular basis. By the end of the article, you will have a fully functional Twitter bot that can keep your followers informed about the latest cryptocurrency prices.

Note: You require a Twitter developer account if you want to use the Twitter functionality. Without an account, you can still print out trading signals to yourself, but you will not be able to post them via the Twitter API.

The code is available on the GitHub repository.

View on GitHub Relataly GitHub Repo

Python Prerequisites

Before starting the coding part, make sure that you have set up your Python 3 environment and required packages. If you don’t have an environment set up yet, you can follow this tutorial to set up the Anaconda environment.

Also, make sure you install all required packages. In this tutorial, we will be working with the following standard packages:

pandas
NumPy

In addition, we will use the following two packages:

Firstly, the gate.io package (package name gate-API) pulls crypto price data from gate.io.
Secondly, we will use the Twitter API library Tweepy to post trading signals via the Twitter API.

You can install packages using console commands:

pip install
conda install (if you are using the anaconda packet manager)

Step #1: Regular Retrieval of Price Data

First, we will define a “prices” class to handle the incoming data flow. The prices class contains a “get_latest_prices” attribute that retrieves price information from gate.io. The function regularly calls the gate.io list_ticker market endpoint.

The list_ticker endpoint returns a list of data fields for cryptocurrency pairs. Examples of price pairs are BTC_USD, BTC_ETH, BTC_ADA, etc. We can limit the response to a single price pair by passing a single pair as a variable in the API call. However, it is not possible to restrict the response to multiple pairs. We either get data for a single pair or all pairs. The response contains a list of the following data fields:

Overview of the data fields in the response

The following code maintains a separate dictionary for each cryptocurrency pair. The dictionary contains the name of the cryptocurrency pair and a data frame that includes the price data history. Each time the crypto bot receives a new response from the API, it goes through the response, extracts the price data(Price, Volume, etc.), and appends this data to the Data Frame of the respective cryptocurrency pair. Then the information is passed to the preprocessing module.

import pandas as pd
import numpy as np
import json
import requests
import datetime as dt
import logging
import threading
import time
from __future__ import print_function

import tweepy 
import gate_api
from gate_api.exceptions import ApiException, GateApiException
from twitter_secrets import twitter_secrets as ts # place the twitter_secrets file under /anaconda3/Lib

class Prices:
    """Class that uses the gate api to retrieve currency data."""

    def __init__(self, config):
        self._config = config
        self._logger = logging.getLogger(__name__)
        configuration = gate_api.Configuration(host="https://api.gateio.ws/api/v4")
        api_client = gate_api.ApiClient(configuration)
        self._api_instance = gate_api.SpotApi(api_client)
        self._price_history = {}
        self._cont_update_thread = None
        self._stop_cont_update_thread = None
        self._price_history_lock = threading.Lock()

    def get_price_history(self):
        """Returns a dictionary with the price histories for the currencies."""
        return self._price_history, self._price_history_lock

    def get_latest_prices(self):
        """Gets new price data and adds the values to a DataFrame.

        Returns the DataFrame in a dictionary with the currencies as keys."""
        timestamp = dt.datetime.now()
        try:
            api_response = self._api_instance.list_tickers()
        except GateApiException as e:
            logging.warning(
                "Gate api exception, label: %s, message: %s\n" % (e.label, e.message)
            )
            return {}
        except ApiException as e:
            logging.warning("Exception when calling SpotApi->list_tickers: %s\n" % e)
            return {}
        latest_prices = {}
        for response in api_response:
            currency = response.currency_pair
            if "USDT" not in currency or "BEAR" in currency:
                continue
            value_dict = {
                "base_volume": pd.to_numeric(response.base_volume),
                "change_percentage": pd.to_numeric(response.change_percentage),
                "etf_leverage": pd.to_numeric(response.etf_leverage),
                "etf_net_value": pd.to_numeric(response.etf_net_value),
                "etf_pre_net_value": pd.to_numeric(response.etf_pre_net_value),
                "etf_pre_timestamp": response.etf_pre_timestamp,
                "high_24h": pd.to_numeric(response.high_24h),
                "highest_bid": pd.to_numeric(response.highest_bid),
                "high_bid": pd.to_numeric(response.highest_bid),
                "last": pd.to_numeric(response.last),
                "low_24h": pd.to_numeric(response.low_24h),
                "lowest_ask": pd.to_numeric(response.lowest_ask),
                "quote_volume": pd.to_numeric(response.quote_volume),
                "timestamp": timestamp,
            }
            latest_prices[currency] = pd.DataFrame(value_dict, index=[1])
        return latest_prices

    def start_cont_update(self):
        self._stop_cont_update_thread = threading.Event()
        self._stop_cont_update_thread.clear()
        self._cont_update_thread = threading.Thread(
            target=self._cont_update,
            args=(
                self._stop_cont_update_thread,
                self._price_history_lock,
            ),
        )
        self._cont_update_thread.start()
        self._logger.info("Started continuous price logging")

    def _cont_update(self, stop_event, lock):
        """Continuously adds new prices to the price history."""
        while not stop_event.is_set():
            start_time = time.time()
            lock.acquire()
            for currency, df in self.get_latest_prices().items():
                if currency in self._price_history.keys():
                    self._price_history[currency] = self._price_history[
                        currency
                    ].append(df, ignore_index=True)
                else:
                    self._price_history[currency] = df
            lock.release()
            self._logger.debug("Currency_dfs updated")
            self._wait_before_update(start_time)

    def _wait_before_update(self, start_time):
        elapsed_time = time.time() - start_time
        self._logger.debug(f"Elapsed time: {elapsed_time}")
        if elapsed_time > self._config["price_update_delay"]:
            delay = 0
            self._logger.warning(
                #"It took longer to retrieve the price data than the update_delay!"
            )
        else:
            delay = self._config["price_update_delay"] - elapsed_time
        self._logger.debug(f"Waiting {delay}s until next update")
        time.sleep(delay)

Step #2: Calculate Indicator Values

Next, we will define a few functions that process the regular data inflow from gate.io and calculate indicator values for the different cryptocurrencies.

Absolute price values signal the bot that the price moves up or down. However, our signaling logic will primarily work with thresholds on percentage values. These indicators have a p at the end of the name in the code below.

In addition, we will avoid misleading signals by incorporating moving averages into the signaling logic. Moving averages work on historical data, so we have to hand over the price history when we call the “calc_indicators” function. Furthermore, we take over other indicators from the data frame, including the 24h_low and the 24h_high. These indicators give us additional information about the indicators of the preceding price points. We can use them to build more robust trading signals.

All indicators are calculated separately for each crypto pair, passed to a dictionary, and then passed to the signaling logic. In the next step, we can use these indicator values in our signaling rules.

def calc_indicators(price_history):
    indicators = {}
    indicators_over_all = calc_indicators_over_all(price_history)
    for currency, df in price_history.items():
        if len(df) <= 2:
            logging.getLogger().debug(
                f"Skipped '{currency} when calculating indicators due to a lack of information"
            )
            continue
        volume = df["base_volume"].iloc[-1]
        last_price = df["last"].iloc[-1]
        moving_avg_price = df["last"].mean()
        moving_average_volume = df["base_volume"].mean()
        moving_average_deviation_percent = np.round(
            div(last_price, moving_avg_price) - 1, 2
        )

        price_before = df["last"].iloc[-2]
        price_delta = last_price - price_before
        price_delta_p = div(price_delta, last_price)
        price_delta_before = price_before - df["last"].iloc[-3]
        price_delta_p_before = div((price_before - df["last"].iloc[-3]), price_before)
        low_24h = df["low_24h"].iloc[-1]
        high_24h = df["high_24h"].iloc[-1]
        low_high_diff_p = div(high_24h - low_24h, low_24h)
        change_percentage = df["change_percentage"].iloc[-1]

        indicator_values = {
            "last_price": last_price,
            "price_before": price_before,
            "volume": volume,
            "moving_avg_price": moving_avg_price,
            "moving_average_volume": moving_average_volume,
            "moving_average_deviation_percent": moving_average_deviation_percent,
            "price_delta_p": price_delta_p,
            "price_delta": price_delta,
            "price_delta_before": price_delta_before,
            "price_delta_p_before": price_delta_p_before,
            "high_24h": high_24h,
            "low_24h": low_24h,
            "low_high_diff_p": low_high_diff_p,
            "change_percentage": change_percentage,
        }
        indicator_values.update(indicators_over_all)
        indicators[currency] = indicator_values
    return indicators


def calc_indicators_over_all(price_history):
    avg_change_p = 0
    for currency, df in price_history.items():
        avg_change_p += df["change_percentage"].iloc[-1]
    nr_of_currencies = len(price_history)
    avg_change_p = div(avg_change_p, nr_of_currencies)
    values = {
        "avg_change_p": avg_change_p,
    }
    return values


def div(dividend, divisor, alt_value=0.0):
    return dividend / divisor if divisor != 0 else alt_value

Step #3: Define the Signaling Logic of the Twitter Bot

Our bot will use a signaling logic that differentiates between the following price signals:

A simple uptick: Price_delta_p must be higher than the threshold (10%) to trigger.
A simple downtick: Price_delta_p must be lower than the threshold (10%) to trigger.
The bot does also report on new 24-hour lows and highs
Another event on which the bot reports is when an up or down price trend begins to accelerate or slows down.
The bot reports that when a price performs a trend reversal (pullback and recovery)

Overview of the different trading signals generated by the signaling logic

Be aware that the price_delta_p measures the percentage deviation from the previous price point. Thus, the signaling logic that our bot has in place is very dependent on the interval in which the bots request new price data. Shorter time intervals will have a lower chance of triggering because more considerable changes typically occur over a longer time. For more details regarding the signaling logic, please view the code below.

def check_signal(currency, indicators, cs_config):
    ind = indicators[currency]
    signal = ''
    if (ind['moving_avg_price'] > 0
            and ind['last_price'] > 0.0
            and abs(ind['price_delta']) > 0.0
            and abs(ind['price_delta_p']) >= cs_config["delta_threshold_p"]
            and ind['volume'] > 0
    ):
        # up
        if ind['price_delta'] > 0:
            movement_type = 'up +'
            if abs(ind['price_delta_p_before']) > cs_config["delta_threshold_p"]:
                if ind['price_delta_before'] <= 0:
                    movement_type = 'recovery from ' + str(ind['price_before']) + ' to ' + str(ind['last_price'])
                else:
                    if ind['price_delta_p'] * (1-cs_config["delta_threshold_p"]) > ind['price_delta_p_before']:
                        movement_type = 'upward trend accelerates +'
                    elif ind['price_delta_p'] < ind['price_delta_p_before'] * (1-cs_config["delta_threshold_p"]):
                        movement_type = 'upward trend slows down +'
                    elif ind['price_delta_p'] * (1+cs_config["delta_threshold_p"]) >= ind['price_delta_p_before'] >= ind['price_delta_p'] * (1-cs_config["delta_threshold_p"]):
                        movement_type = 'upward trend continues +'
        # down
        elif ind['price_delta'] < 0:
            movement_type = 'down '
            if abs(ind['price_delta_p_before']) > cs_config["delta_threshold_p"]:
                if ind['price_delta_before'] > 0:
                    movement_type = 'pullback from ' + str(ind['price_before']) + ' to ' + str(ind['last_price'])
                else:
                    if ind['price_delta_p'] * (1-cs_config["delta_threshold_p"]) > ind['price_delta_p_before']:
                        movement_type = 'down trend accelerates '
                    elif ind['price_delta_p'] * (1+cs_config["delta_threshold_p"]) >= ind['price_delta_p_before'] >= ind['price_delta_p'] * (1-cs_config["delta_threshold_p"]):
                        movement_type = 'down trend continues '
                    elif ind['price_delta_p'] < ind['price_delta_p_before'] * (1+cs_config["delta_threshold_p"]):
                        movement_type = 'downward trend slows down '

        signal = get_signal_log(movement_type, currency, ind['price_delta_p'], ind['last_price'],
                                ind['moving_avg_price'], ind['volume'], ind['price_delta'], ind['change_percentage'],
                                ind['high_24h'], ind['low_24h'], ind['low_high_diff_p'])

        check_24h_peak(currency, ind['last_price'], ind['low_24h'], ind['high_24h'])

    return signal
    # trade_signal


def check_24h_peak(currency, last_price, low_24h, high_24h):
    if last_price < low_24h:
        print(currency + ' new 24h low $' + str(last_price))
    elif last_price > high_24h:
        print(currency + ' new 24h high $' + str(last_price))


def get_signal_log(movement_type, currency, price_delta_p, last_price, moving_avg_price, volume, price_delta,
                   daily_up_p, high_24h, low_24h, low_high_diff_p):
    signal = f'{currency} {movement_type} ' \
             f'{np.round(price_delta_p * 100, 5)}% ' \
             f'MA:${np.round(moving_avg_price, 6)} ' \
             f'last_price:${np.round(last_price, 6)} ' \
             f'price delta:{np.round(price_delta, 6)} ' \
             f'volume:${np.round(volume, 1)} ' \
             f'daily_change:{np.round(daily_up_p, 2)}% ' \
             f'high_24h:${high_24h} ' \
             f'low_24h:${low_24h} ' \
             f'low_high_diff_p:{np.round(low_high_diff_p * 100, 2)}%'
    return signal

Step #4: Send Tweets via Twitter

Next, we define a simple function that calls the Twitter API and tweets our price signal. Because the Twitter API requires authentication, you must provide the API authentication credentials from a valid Twitter developer account.

It’s best not to store the API credentials directly in code. Still not perfect, but slightly better is to keep the data in a separate python file (for example, called “twitter_secrets”) that you put into your package folder (for example, under /anaconda3/Lib), from where you can import it directly into your code.

# Twitter Consumer API keys
CONSUMER_KEY    = "api123"
CONSUMER_SECRET = "api123"

# Twitter Access token & access token secret
ACCESS_TOKEN    = "api123"
ACCESS_SECRET   = "api123"

BEARER_TOKEN = "api123"

class TwitterSecrets:
    """Class that holds Twitter Secrets"""

    def __init__(self):
        self.CONSUMER_KEY    = CONSUMER_KEY
        self.CONSUMER_SECRET = CONSUMER_SECRET
        self.ACCESS_TOKEN    = ACCESS_TOKEN
        self.ACCESS_SECRET   = ACCESS_SECRET
        self.BEARER_TOKEN   = BEARER_TOKEN
        
        # Tests if keys are present
        for key, secret in self.__dict__.items():
            assert secret != "", f"Please provide a valid secret for: {key}"

twitter_secrets = TwitterSecrets()

Once you have imported the file, you can then load the API credentials from the file in the following way:

consumer_key = ts.CONSUMER_KEY
consumer_secret = ts.CONSUMER_SECRET
access_token = ts.ACCESS_TOKEN
access_secret = ts.ACCESS_SECRET

### Print API Auth Data (leave disabled for security reasons)
# print(f'consumer_key: {consumer_key}')
# print(f'consumer_secret: {consumer_secret}')
# print(f'access_token: {access_token}')
# print(f'access_secret: {access_token}')

#authenticating to access the twitter API
auth=tweepy.OAuthHandler(consumer_key,consumer_secret)
auth.set_access_token(access_token,access_secret)
api=tweepy.API(auth)

def send_pricechange_tweet(signal):
    api.update_status(f"{signal} \n {relataly_url}")

Step #5 Starting the Crypto Signal Bot

Finally, we can hit the start button of our crypto signal bot. But before we do this, take a look at some configuration options of the bot.

CYCLE_DELAY is the standard interval in seconds in which the bot will call the gate.io API.
CURRENCY_PAIR is another API parameter limiting the cryptocurrency pairs to specific currency pairs. The bot will scan the entire market with all currency pairs in the standard setting, including all USDT pairs.
TWITTER_ACTIVE defines whether the bot posts signals on Twitter. Be aware that your bot may instantly report any signal on your Twitter account if you enable it.
RUNS defines the max number of prices that the bot will retrieve before the bot stops.

Now, let’s test the bot:

RUNS = 50 # the bot will stop after 50 price points
CYCLE_DELAY = 20 # the interval for checking the data and retrieving another price point
EVAL_PRICES_DELAY = 10
CURRENCY_PAIR = "" # the bot will retrieve data for all currency pairs listed on gate.io
PRICES_CONFIG = {"price_update_delay": 20}
TWITTER_ACTIVE = False

CHECK_SIGNAL_CONFIG = {
    "moving_avg_threshold_down_p": 0.10,
    "moving_avg_threshold_up_p": 0.10,
    "delta_threshold_p": 0.07,
    'enable_twitter': TWITTER_ACTIVE,
}

if __name__ == "__main__":
    logging.basicConfig(
        level=logging.INFO, format="\033[02m%(asctime)s %(levelname)s: %(message)s"
    )
    logger = logging.getLogger(__name__)
    prices = Prices(PRICES_CONFIG)
    prices.start_cont_update()
    currency_dfs = {}
    logging.info(f"Crypto bot is starting - please wait")
    logger.info(f"Collecting crypto data from gate.io for {EVAL_PRICES_DELAY}s")
    time.sleep(EVAL_PRICES_DELAY)
    logger.info(f"\n<< Crypto signal bot started :-) >>")
    logger.info(f"<< Checking prices every {CYCLE_DELAY} seconds >>")
    logger.info(f"Now checking for signals - please wait\n")
    for i in range(RUNS):
        price_history, lock = prices.get_price_history()
        lock.acquire()
        indicators = calc_indicators(price_history)
        lock.release()
        for currency in indicators.keys():
            if not indicators[currency]:
                continue
            signal = check_signal(
                currency,
                indicators,
                CHECK_SIGNAL_CONFIG,
            )
            if signal:
                logger.info(signal)
                if CHECK_SIGNAL_CONFIG['enable_twitter']:
                    send_pricechange_tweet(signal)
                    print('send via twitter')
        time.sleep(CYCLE_DELAY)

2022-03-09 11:40:38,939 INFO: Started continuous price logging
2022-03-09 11:40:38,940 INFO: Crypto bot is starting - please wait
2022-03-09 11:40:38,940 INFO: Collecting crypto data from gate.io for 10s
2022-03-09 11:40:48,941 INFO: 
<< Crypto signal bot started :-) >>
2022-03-09 11:40:48,942 INFO: << Checking prices every 20 seconds >>
2022-03-09 11:40:48,942 INFO: Now checking for signals - please wait

2022-03-09 11:52:06,800 INFO: EOSBULL_USDT up + 19.42446% MA:$1.1e-05 last_price:$1.4e-05 price delta:3e-06 volume:$1272326905.1 daily_change:33.65% high_24h:$1.16e-05 low_24h:$9.8e-06low_high_diff_p:18.37%
EOSBULL_USDT new 24h high $1.39e-05
send via twitter

And this is what the tweets will look like on Twitter:

Summary

Congratulations on completing this tutorial! In this article, you learned how to build a Python-based Twitter crypto signal bot. When run, the bot will regularly retrieve cryptocurrency quotes from the Gate.io exchange and tweet about any price movements based on a simple signaling logic.

While the signaling logic in this tutorial is kept simple, this basic framework provides a foundation for you to further develop and enhance the signaling rules. For example, you could consider using volume or price volatility changes as the basis for defining signals. Have fun experimenting and expanding upon this project!

If you found this article helpful, please show your appreciation by leaving a comment. Cheers

Sources and Further Reading

The links above to Amazon are affiliate links. By buying through these links, you support the Relataly.com blog and help to cover the hosting costs. Using the links does not affect the price.

The post Automate Crypto Trading with a Python-Powered Twitter Bot and Gate.io Signals appeared first on relataly.com.

Requesting Crypto Prices from the Coinmarketcap API using Python

Florian Follonier — Sun, 18 Apr 2021 21:11:29 +0000

You can do various things with cryptocurrency price data, such as creating forecasting models, illustrating historical prices, or performing chart analysis. But first, you need to get hold of the data. Nowadays, several APIs provide access to cryptocurrency price data. One of the most important and trusted sources is Coinmarketcap.com. The website offers multiple API endpoints that provide access to historical price data for various cryptocurrencies like Bitcoin, Ethereum, etc. In addition, Coinmarketap offers meta information about coins and crypto exchanges, including listing and halving dates, circulating supply, or the current market cap. This tutorial gives an overview of the coinmarketcap API and shows how to use the API with Python. We will request historical price data and store them in a local SQLite database using the Pewee SQL framework.

The rest of this article is a Python hands-on tutorial: First, we lay the groundwork for working with the Coinmarketcap API by signing up for a free API key. Subsequently, we store this in a YAML file and use it to query bitcoin quotes from the Coinmarketcap API endpoint. Our goal is to store the data with Peewee in an SQLite DB. We will first define a schema to store the data. Finally, we will learn how to query the obtained rates from our local database and use them later, for example, to train a price prediction model.

Also: Streaming Tweets and Images via the Twitter API in Python

Coinmarketcap offers a REST API through which we can access Crypto Price data.

The Coinmarketcap API

CoinMarketCap is one of the world’s most referenced crypto asset price tracking websites. The website provides high-quality and accurate information to private and business users so they can draw their own informed conclusions. In April 2020, CoinMarketCap was acquired by Binance – the largest cryptocurrency exchange in the world.

Generally, all relevant coins are listed on Coinmarketcap – however, some are scam coins and coins whose further development was abandoned long ago. The CoinMarketCap API is a set of powerful RESTful JSON endpoints. According to the website, these APIs are designed to meet the mission-critical needs of application developers, data scientists, and enterprise platforms.

Let’s look at some of the available REST endpoints.

Coinmarketcap API documentation provides a comprehensive overview of the available API functions.

Endpoints

Coinmarketcap offers several API endpoints for various crypto-related data, including prices, exchanges, and other information.

Below is a selection of API endpoints:

Endpoint	When to Use?
/cryptocurrency/*	To retrieve price and volume data (aggregated over all exchanges) or cryptocurrency-related price data.
/exchange/*	To return exchange-specific data such as ordered exchange lists and market pair data.
/global-metrics/*	To return, obtain aggregate statistics such as global market cap and BTC dominance.
/blockchain/*	For obtaining blockchain analytics data, e.g., transaction volume, etc.
/key/*	For managing and monitoring API key usage.

Some of the API endpoints available via coinmarketcap

A big advantage of the Coinmarketcap API is that the data goes way back. For example, the historical price data for Bitcoin ranges back to 2013.

Acquiring an API Key from the Developer Portal

All requests to the coinmarketcap API require authentication with an API key. Authentication can be achieved in two ways:

Send the API key in the custom header of the HTTP request with the attribute ‘X-CMC_PRO_API_KEY’ (Prefered).
Provide the key in the URL path.

Before requesting data from the coinmarketcap API, you must acquire your API key by registering on the coinmarketcap API developer portal. You will choose from one of several pricing plans during the registration process.

The Free basic plan provides access to most data and is sufficient for this tutorial. However, it has severe limitations, such as a limited number of requests you can make daily and monthly. After the registration, you can log in to your account and display the API key in the “Overview” section.

Each call to the Coinmarketcap API consumes API credits. The number of calls and data we can retrieve via API is limited to 100 credits for the free plan. You can see how many of your daily and monthly credits are still available on the overview page.

Different pricing plans of the Coinmarketcap API

Overview page of the coinmarketcap API

Storing the API Key

It would be best never to store an API key directly in the code for security reasons. A better practice is to import and access the API key from a separate YAML file. Create the file with the name “api_config_coinmarketcap.yml” and insert your API key into this file as follow:

api_key: “your coinmarketcap api key”

Obtaining Bitcoin Prices via the Coinmarketcap API

In this tutorial, we demonstrate how to use Python to send periodic requests to the Coinmarketcap API to obtain cryptocurrency prices for the top-ranked digital asset by market capitalization. Since each request to the Coinmarketcap API consumes credits, we utilize a local SQLite database to store the price data. This allows us to retrieve the data without the need for additional API requests and preserves credits for future use.

To efficiently manage the SQLite database, we employ the PeeWee framework, which offers a robust and user-friendly interface for interacting with SQL databases in Python. This approach enables users to easily create, modify, and query data within the SQLite database.

Overall, this tutorial provides a valuable resource for anyone looking to automate the collection and storage of cryptocurrency price data using the Coinmarketcap API and SQLite database with the Pewee framework.

bitcoin and other cryptocurrencies relataly midjourney python tutorial coinmarketcap-min

" data-image-caption="

bitcoin and other cryptocurrencies relataly midjourney python tutorial coinmarketcap-min

" data-large-file="https://www.relataly.com/wp-content/uploads/2023/03/bitcoin-and-other-cryptocurrencies-relataly-midjourney-python-tutorial-coinmarketcap-min.png" src="https://www.relataly.com/wp-content/uploads/2023/03/bitcoin-and-other-cryptocurrencies-relataly-midjourney-python-tutorial-coinmarketcap-min-512x512.png" alt="bitcoin and other cryptocurrencies relataly midjourney python tutorial coinmarketcap " class="wp-image-12618" srcset="https://www.relataly.com/wp-content/uploads/2023/03/bitcoin-and-other-cryptocurrencies-relataly-midjourney-python-tutorial-coinmarketcap-min.png 512w, https://www.relataly.com/wp-content/uploads/2023/03/bitcoin-and-other-cryptocurrencies-relataly-midjourney-python-tutorial-coinmarketcap-min.png 300w, https://www.relataly.com/wp-content/uploads/2023/03/bitcoin-and-other-cryptocurrencies-relataly-midjourney-python-tutorial-coinmarketcap-min.png 140w, https://www.relataly.com/wp-content/uploads/2023/03/bitcoin-and-other-cryptocurrencies-relataly-midjourney-python-tutorial-coinmarketcap-min.png 768w, https://www.relataly.com/wp-content/uploads/2023/03/bitcoin-and-other-cryptocurrencies-relataly-midjourney-python-tutorial-coinmarketcap-min.png 1024w" sizes="(max-width: 512px) 100vw, 512px" />

The code is available on the GitHub repository.

View on GitHub Relataly Github Repo

Prerequisites

Before beginning the coding part, please ensure you have set up your Python 3 environment and required packages. If you don’t have a Python environment available yet, you can follow the steps in this article to set up the Anaconda Python environment.

Also, make sure you install the required packages. In this tutorial, we will be working with the following standard packages:

In addition, we will use Peewee, a lightweight Object-Relational-Mapper (ORM) framework that lets us interact with SQLite in an object-oriented and more convenient way.

You can install packages using console commands:

pip install
conda install (if you are using the anaconda packet manager)

Step #1 Create a Relational Model in SQLite

We begin by defining a relational data model in SQlite using Pewee. We will then use this model to persist the crypto price data from the API and make them available for later reuse. Our model will contain two tables:

Run: A table where we will store an ID and a timestamp makes identifying past runs easier. We start querying data from the API, we will add a new runid to this table.
The second table is called price and contains the price quotes for a predetermined interval. We will store the price quotes in this table and reference the runid via a foreign key.

Running the code below creates empty tables.

from peewee import *
from datetime import date, timedelta, datetime
import numpy as np 
import pandas as pd
import seaborn as sns
import time
import json
import yaml
from requests import Request, Session
from requests.exceptions import ConnectionError, Timeout, TooManyRedirects
from pandas.plotting import register_matplotlib_converters

import matplotlib.dates as mdates
import matplotlib.pyplot as plt

# persist information
db = SqliteDatabase('assets.db')

class BaseModel(Model):
    class Meta:
        database = db

class Run(BaseModel): # This model uses the "assets.db" database.
    timestamp = DateTimeField(unique=True)
    symbol = CharField()
    id = AutoField()

class Price(BaseModel): # This model uses the "assets.db" database.
    datetime = DateTimeField(unique=True)
    volume_24 = FloatField()
    price = FloatField()
    runid = ForeignKeyField(Run, backref='prices')
         
# By default, Peewee includes an IF NOT EXISTS clause when creating tables. 
def create_tables():
    with db:
        db.create_tables([Price])
        db.create_tables([Run])

def drop_tables():
    with db:
        db.drop_tables([Price])
        db.drop_tables([Run])
        
create_tables()

#drop_tables()

If you want to drop the tables again, you can call our custom drop_tables function.

Step #2 Streaming Price Quotes

Now that we have created the relational data model, we can define a request to the Coinmarketcap API and initiate the stream of price data. For this purpose, we will call the coinmarketcap REST endpoint “cryptocurrency/listings/latest.” This endpoint returns a JSON response that contains the latest price data for a specific number of cryptocurrencies based on their market capitalization rank. The response contains the following information for each cryptocurrency included:

Price value
The timestamp
The trading volume
The cost of the request in credits

Before querying the API, we need to define basic API parameters:

Conversion_Currency: The currency in which the API will return the prices.
Start: The position in the market cap ranking from which the API will include cryptocurrencies in the result.
Limit: The number of cryptocurrencies for which the API will return prices based on the start position in the current market cap ranking.

We will select USD as “conversion_currency,” set the limit, and start to “one.” As a result, the API will return only the price of Bitcoin. We use a query interval of 10 seconds. You can change the interval, but be aware that you will deplete your free credits quickly if you request data by the second. We will therefore limit the requests to 200. You can increase the timeframe to retrieve price quotes for more extended periods.

We will store and load our API key from a YAML file. Be sure to replace the api_key_path in the code below with the path where you placed the api_config_coinmarketcap.yml file. Once you have made this adjustment, you can start the API requests by running the code below.

symbol='BTCUSD'
query_interval = 10 # in seconds
query_number = 100 # the number of queries after which the API stops

# load the API key from our local file
with open(r'API Keys/api_config_coinmarketcap.yml') as file:
    apikey = yaml.load(file, Loader=yaml.FullLoader)['api_key']

# Define some essential API parameters
# Coinmarketcap API for latest market ticker quotes and averages for cryptocurrencies and exchanges.
# https://coinmarketcap.com/api/documentation/v1/#operation/getV1CryptocurrencyListingsLatest
url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
parameters = {
  'start':'1',
  'limit':'1',
  'convert':'USD'
}
headers = {
  'Accepts': 'application/json',
  'X-CMC_PRO_API_KEY': apikey,
}

session = Session()
session.headers.update(headers)

# create a new run and save it to our local SQLite DB
run_timestamp = datetime.now()
run = Run(symbol=symbol, timestamp = run_timestamp)
run.save()
current_run_id = run.id 
print(f'run_id: {current_run_id} - timestamp: {run_timestamp} - interval: {query_interval} - number of queries: {query_number}')

# query the coinmarketcap API every x seconds
for s in range(0, query_number):
    try:
        response = session.get(url, params=parameters)
        data = json.loads(response.text)
        #print(data)
        
        # response - quote
        data_quote = data['data'][0]['quote']['USD']
        price = np.round(data_quote['price'], 1) # the price
        volume_24 = np.round(data_quote['volume_24h'], 1) # the volume in the last 24 hours
        
        # response - status
        data_status = data['status']
        api_timestamp = data_status['timestamp'] # the timestamp of the pricepoint
        api_credits = data_status['credit_count'] # the number of credits spent on the last request
        
        # create a new pricepoint and save it to the SQLite db
        new_pricepoint = Price(datetime=api_timestamp, price=price, volume_24=volume_24, runid=current_run_id)
        id = new_pricepoint.save()

        # display what we have just saved
        print(f'request number: {s} - added {price} at {api_timestamp} - 24 hour volume: {volume_24} - credits used: {api_credits}')      

    except (ConnectionError, Timeout, TooManyRedirects) as e:
        print(e)
    time.sleep(query_interval)
print('finished')

run_id: 2 - timestamp: 2021-05-03 22:03:01.172363 - interval: 5 - number of queries: 200
request number: 0 - added 57487.7 at 2021-05-03T20:03:02.587Z - 24 hour volume: 48962167377.0 - credits used: 1
request number: 1 - added 57487.7 at 2021-05-03T20:03:08.059Z - 24 hour volume: 48962167377.0 - credits used: 1
request number: 2 - added 57487.7 at 2021-05-03T20:03:13.256Z - 24 hour volume: 48962167377.0 - credits used: 1
request number: 3 - added 57453.4 at 2021-05-03T20:03:18.470Z - 24 hour volume: 48948337250.5 - credits used: 1

Step #3 Querying the Data from Our SQL Table

We now have the price data persisted in our local SQLite DB. From there, we can query the data using the peewee “select” SQL command. For example, we will query the run table and retrieve the number of requests made to the coinmarketcap API.

query = Run.select().where(Run.id==current_run_id)
run_overview = pd.DataFrame(list(query.dicts()))
run_overview

Next, select the price quotes for this specific run-id and display them in a line plot.

query = Price.select().where(Price.runid==current_run_id)
df_prices = pd.DataFrame(list(query.dicts()))
print(df_prices)

register_matplotlib_converters()
fig, ax = plt.subplots(figsize=(10, 8))
plt.title('BTC prices from the coinmarketcap API', fontsize=14)
datetimes = pd.to_datetime(df_prices['datetime'])

sns.lineplot(data=df_prices, x=datetimes, y="price")
#ax.set_xlim([df_prices['datetime'].min(),df_prices['datetime'].max()])
#ax.xaxis.set_major_locator(mdates.MinuteLocator())
plt.xticks(rotation=75)

And voila, the line plot shows the last data received from the API. We now have stored this data in our SQLite database.

Summary

This article gave a quick overview of the Coinmarketcap API – one of the most referenced APIs in the crypto space. You have learned to request historical price data from a Coinmarketcap API endpoint with Python. We wrote a short Python script that queries one of several REST endpoints (cryptocurrency/listings/latest) in regular intervals. To ensure that we can later reuse the requested data, we stored the response from the API in an SQLite database using the Pewee framework.

I am always happy to receive feedback from my readers. So if the tutorial helped you, or if you have any comments, please write them in the comments.

Sources and Further Reading

The links above to Amazon are affiliate links. By buying through these links, you support the Relataly.com blog and help to cover the hosting costs. Using the links does not affect the price.

If you are not sure yet, if the coinmarketcap API is for you, consider these other ways of obtaining cryptocurrency price data:

For more Python tutorials on stock-market prediction, check out the following articles:

The post Requesting Crypto Prices from the Coinmarketcap API using Python appeared first on relataly.com.

Forecasting Beer Sales with ARIMA in Python

Florian Follonier — Wed, 03 Feb 2021 22:23:08 +0000

Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear pattern, like seasonal changes in weather or sales. ARIMA has been used to forecast everything from beer sales to order quantities, and this tutorial will show you how to build your own ARIMA model in Python. You’ll be making predictions like a pro in no time!

This tutorial proceeds in two parts: The first part covers the concepts behind ARIMA. You will learn how ARIMA works, what Stationarity means, and when it is appropriate to use ARIMA. The second part is a Python hands-on tutorial that applies auto-ARIMA to the Sales Forecasting domain. We’ll be working with a time series of beer sales, and our goal is to predict how the beer sales quantities will evolve in the coming years. First, we check if the time series is stationary. Then we train an ARIMA forecasting model. Finally, we use the model to produce a sales forecast and measure the model’s performance.

About Sales Forecasting

Sales forecasting is a crucial business strategy that involves predicting future sales volumes for a product (for example, beer) or service. It leverages sophisticated statistical and analytical techniques, such as time series analysis or machine learning algorithms, to scrutinize historical sales data. By identifying trends and patterns within this data, businesses can make informed predictions about their future sales performance.

This strategic forecasting plays a pivotal role in business operations. It is instrumental in guiding key decisions surrounding production, inventory management, staffing, and various other operational elements. By honing in on accurate sales forecasting, businesses can strike the perfect balance – maintaining enough inventory to meet customer demand without overproducing or overstocking. This equilibrium ensures a smooth flow in the supply chain and avoids unnecessary costs tied to excess production or storage.

Furthermore, sales forecasting serves as a roadmap for business growth. It aids in identifying potential market opportunities and predicting future sales revenue. This valuable foresight enables businesses to strategically plan their expansion, ensuring resources are optimally utilized and future goals are met. With this in-depth understanding of sales forecasting, businesses can stay ahead of market trends, navigate through business challenges, and ultimately steer towards success.

Businesses rely on sales forecasting to make informed decisions about production, inventory management, staffing, and other key operational aspects. Image created with Midjourney

" data-image-caption="

Businesses rely on sales forecasting to make informed decisions about production, inventory management, staffing, and other key operational aspects. Image created with Midjourney

" data-large-file="https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly-min.png" src="https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly-min.png" alt="Businesses rely on sales forecasting to make informed decisions about production, inventory management, staffing, and other key operational aspects. Image created with Midjourney" class="wp-image-12602" srcset="https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly-min.png 506w, https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly-min.png 300w, https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly-min.png 140w" sizes="(max-width: 506px) 100vw, 506px" />

Businesses rely on sales forecasting to make informed decisions about production, inventory management, staffing, and other key operational aspects. Image created with Midjourney

Introduction to ARIMA Time Series Modelling

ARIMA models provide an alternative approach to time series forecasting that differs significantly from machine learning methods. Working with ARIMA requires a good understanding of Stationarity and knowledge of the transformations used to make time-series data stationary. The concept of Stationarity is, therefore, first on our schedule.

The Concept of Stationarity

Stationarity is an essential concept in stochastic processes that describes the nature of a time series. We consider a time series strictly stationary if its statistical properties do not change over time. In this case, summary statistics, such as the mean and variance, do not change over time. However, the time-series data we encounter in the real world often show a trend or significant irregular fluctuations, making them non-stationary or weakly stationary.

So why is Stationarity such an essential concept for ARIMA? If a time series is stationary, we can assume that the past values of the time series are predictive of future development. In other words, a stationary time series exhibits consistent behavior that makes it predictable. On the other hand, a non-stationary time series is characterized by a kind of random behavior that will be difficult to capture in modeling. Namely, if random movements characterized the past, there is a high probability that the future will be no different.

Fortunately, in many cases, it is possible to transform a time series that is non-stationary into a stationary form and, in this way, build better prediction models.

A stationary Vs. a non-stationary time series

How to Test Whether a Time Series is Stationary

The first step in the ARIMA modeling approach is determining whether a time series is stationary. There are different ways to determine whether a time series is stationary:

Plotting: We can plot the time series and visually check if it shows consistent behavior or changes over a more extended period.
Summary statistics: We can split the time series into different periods and calculate the summary statistics, such as the variance. If these metrics are subject to significant changes, the time series is non-stationary. However, the results will also depend on the respective periods, leading to false conclusions.
Statistic tests: There are various tests to determine the stationary of a time series, such as Kwiatkowski–Phillips–Schmidt–Shin, Augmented Dickey-Fuller, or Phillips–Perron. These tests systematically check a time series and measure the results against the null hypothesis, providing an indicator of the trustworthiness of the results.

What is an (S)ARIMA Model?

As the name implies, ARIMA uses autoregression (AR), integration (differencing), and moving averages (MA) to fit a linear regression model to a time series.

ARIMA Parameters

The default notation for ARIMA is a model with parameters p, d, and q, whereby each parameter takes an integer value:

d (differencing): In the case of a non-stationary time series, there is a chance to remove a trend from the data by differencing once or several times, thus bringing the data to a stationary state. The model parameter d determines the order of the differentiation. A value of d = 0 simplifies the ARIMA model to an ARMA model, lacking the integration aspect. If this is the case, we do not need to integrate the function because the time series is already stationary.
p (order of the AR terms): The autoregressive process describes the dependent relationship between an observation and several lagged observations (lags). Predictions are then based on past data from the same time series using linear functions. p = 1 means the model uses values that lag by one period.
q (order of the MA terms): The parameter q determines the number of lagged forecast errors in the prediction equation. In contrast to the AR process, the MA process assumes that values at a future point in time depend on the errors made by predictions at current and past points in time. This means that it is not previous events that determine the predictions but rather the previous estimation or prediction errors used to calculate the following time series value.

SARIMA

In the real world, many time series have seasonal effects. Examples are monthly retail sales figures, temperature reports, weekly airline passenger data, etc. To consider this, we can specify a seasonal range (e.g., m=12 for monthly data) and additional seasonal AR or MA components for our model that deal with seasonality. Such a model is also called a SARIMA model, and we can define it as a model(p, d, q)(P, D, Q)[m].

Auto-(S)ARIMA

When working with ARIMA, we can set the model parameters manually or use auto-ARIMA and let the model search for the optimal parameters. We do this by varying the parameters and then testing against Stationarity. With the seasonal option enabled, the process tries to identify the optimal hyperparameters for the seasonal components of the model. Auto-ARIMA works by conducting differencing tests to determine the order of differencing, d and then fitting models with parameters in defined ranges, e.g., start_p, max_p as well as start_q, max_q. If our model has a seasonal component, we can also define parameter ranges for the seasonal part of the model.

Creating a Sales Forecast with ARIMA in Python

Having grasped the fundamental concepts behind ARIMA (AutoRegressive Integrated Moving Average), we’re now ready to dive into the practical aspect of crafting a sales forecasting model in Python. Utilizing ARIMA for forecasting sales data is an esteemed practice owing to the algorithm’s adeptness in modeling seasonal changes combined with long-term trends – a characteristic commonly exhibited by sales data.

In this tutorial, we’ll be employing a dataset representing the monthly beer sales across the United States from 1992 through 2018, recorded in millions of US dollars. Our objective is to construct a robust time series model using ARIMA to accurately predict future sales trends.

When it comes to the technological aspect, we’ll be using the Python-based ‘statsmodels’ and ‘pmdarima’ libraries to build our ARIMA sales forecasting model. So, if you’re ready to harness the power of Python and ARIMA for sales prediction, let’s get started!

The code is available on the GitHub repository.

View on GitHub Relataly GitHub Repo

A fluffy cat drinking beer after creating an ARIMA sales forecast. Image created with Midjourney

Prerequisites

Before we start coding, ensure you have set up your Python 3 environment and required packages. If you don’t have an environment, you can follow this tutorial to set up the Anaconda environment.

Also, make sure you install all required packages. In this tutorial, we will be working with the following standard packages:

In addition, we will be using the statsmodels library and pmdarima.

You can install packages using console commands:

pip install
conda install (if you are using the anaconda packet manager)

Step #1 Load the Sales Data to Our Python Project

In the initial step of this tutorial, we commence by setting up the necessary Python environment. We import several packages that we’ll be using for data manipulation, visualization, and implementing machine learning models. We then fetch the dataset we’ll be working with – the monthly beer sales in the United States from 1992 through 2018. This data is sourced from a publicly accessible URL and loaded into a pandas DataFrame.

# A tutorial for this file is available at www.relataly.com
# Tested with Python 3.88

# Setting up packages for data manipulation and machine learning
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pmdarima as pm
from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.tsa.seasonal import seasonal_decompose
import seaborn as sns
sns.set_style('white', { 'axes.spines.right': False, 'axes.spines.top': False})

# Link to the dataset: 
# https://www.kaggle.com/bulentsiyah/for-simple-exercises-time-series-forecasting

path = "https://raw.githubusercontent.com/flo7up/relataly_data/main/alcohol_sales/BeerWineLiquor.csv"
df = pd.read_csv(path)
df.head()

		date	beer
0	1/1/1992	1509
1	2/1/1992	1541
2	3/1/1992	1597
3	4/1/1992	1675
4	5/1/1992	1822

As shown above, the sales figures in this dataset stem from the first day of each month.

Step #2 Visualize the Time Series and Check it for Stationarity

Before modeling the sales data, we visualize the time series and test it for Stationarity. Visualization helps us choose the parameters for our ARIMA model, thus making it an essential step.

First, we will look at the different components of the time series. We do this by using the seasonal_decompose function of the statsmodels library.

# Decompose the time series
plt.rcParams["figure.figsize"] = (10,6)
result = seasonal_decompose(df['beer'], model='multiplicative', period = 12)
result.plot()
plt.show()

To test for Stationarity, we use the ADFuller test. It is common to run this test multiple times throughout a data science project. Therefore, we create a function that we can then reuse later.

def check_stationarity(df_sales, title_string, labels):
    # Visualize the data
    fig, ax = plt.subplots(figsize=(16, 8))
    plt.title(title_string, fontsize=14)
    if df_sales.index.size > 12:
        df_sales['ma_12_month'] = df_sales['beer'].rolling(window=12).mean()
        df_sales['ma_25_month'] = df_sales['beer'].rolling(window=25).mean()
        sns.lineplot(data=df_sales[['beer', 'ma_25_month', 'ma_12_month']], palette=sns.color_palette("mako_r", 3))
        plt.legend(title='Smoker', loc='upper left', labels=labels)
    else:
        sns.lineplot(data=df_sales[['beer']])
    
    plt.show()
    
    sales = df_sales['beer'].dropna()
    # Perform an Ad Fuller Test
    # the default alpha = .05 stands for a 95% confidence interval
    adf_test = pm.arima.ADFTest(alpha = 0.05) 
    print(adf_test.should_diff(sales))
    
df_sales = pd.DataFrame(df['beer'], columns=['beer'])
df_sales.index = pd.to_datetime(df['date']) 
title = "Beer sales in the US between 1992 and 2018 in million US$/month"
labels = ['beer', 'ma_12_month', 'ma_25_month']
check_stationarity(df_sales, title, labels)

The data does not appear to be stationary. We can see that our time series is steadily increasing and shows annual seasonality. The steady increase indicates a continuous growth in beer consumption over the last decades. The seasonality in the sales data likely results from people drinking more beer in summer than in other seasons.

Step #3 Exemplary Differencing and Autocorrelation

The chart from the previous section shows that our time series is non-stationary. The reason is that it follows a clear upward trend. We also know that the time series has a seasonal component. Therefore, we need to define additional parameters and construct a SARIMA model.

Before we use auto-correlation to determine the optimal parameters, we will try manual differencing to make the time series stationary. There is no guarantee that differencing works. It is essential to remember that differencing can sometimes also worsen prediction performance. So be careful, not to overdifference! We could also trust that the auto-ARIMA model chooses the best parameters for us. However, we should always validate the selected parameters.

The ideal differencing parameter is the least number of differencing steps to achieve a stationary time series. We will monitor the results with autocorrelation plots to check whether differencing was successful.

We print the autocorrelation for the original time series and after the first and second-order differencing.

# 3.1 Non-seasonal part
def auto_correlation(df, prefix, lags):
    plt.rcParams.update({'figure.figsize':(7,7), 'figure.dpi':120})
    
    # Define the plot grid
    fig, axes = plt.subplots(3,2, sharex=False)

    # First Difference
    axes[0, 0].plot(df)
    axes[0, 0].set_title('Original' + prefix)
    plot_acf(df, lags=lags, ax=axes[0, 1])

    # First Difference
    df_first_diff = df.diff().dropna()
    axes[1, 0].plot(df_first_diff)
    axes[1, 0].set_title('First Order Difference' + prefix)
    plot_acf(df_first_diff, lags=lags - 1, ax=axes[1, 1])

    # Second Difference
    df_second_diff = df.diff().diff().dropna()
    axes[2, 0].plot(df_second_diff)
    axes[2, 0].set_title('Second Order Difference' + prefix)
    plot_acf(df_second_diff, lags=lags - 2, ax=axes[2, 1])
    plt.tight_layout()
    plt.show()
    
auto_correlation(df_sales['beer'], '', 10)

(0.019143247561160443, False)

The charts above show that the time series becomes stationary after one order differencing. However, we can see that the lag goes into the negative very quickly, which indicates overdifferencing.

Next, we perform the same procedure for the seasonal part of our time series.

# 3.2 Seasonal part

# Reduce the timeframe to a single seasonal period
df_sales_s = df_sales['beer'][0:12]

# Autocorrelation for the seasonal part
auto_correlation(df_sales_s, '', 10)

# Check if the first difference of the seasonal period is stationary
df_diff = pd.DataFrame(df_sales_s.diff())
df_diff.index = pd.date_range(df_sales_s.diff().iloc[1], periods=12, freq='MS') 
check_stationarity(df_diff, "First Difference (Seasonal)", ['difference'])

(0.99, True)

After first order differencing, the seasonal part of the time series is stationary. The autocorrelation plot shows that the values go into the negative but remain within acceptable boundaries. Second-order differencing does not seem to improve these values. Consequently, we conclude that first-order differencing is a good choice for the D parameter.

Step #4 Finding an Optimal Model with Auto-ARIMA

Next, we auto-fit an ARIMA model to our time series. In this way, we ensure that we can later measure the performance of our model against a fresh set of data that the model has not seen so far. We will split our dataset into train and test in preparation for this.

Once we have created the train and test data sets, we can configure the parameters for the auto_arima stepwise optimization. By setting max_d = 1, we tell the model to test no-differencing and first-order differencing. Also, we set max_p and max_q to 3.

To deal with the seasonality in our time series, we set the “seasonal” parameter to True and the “m” parameter to 12 data points. We turn our model into a SARIMA model that allows us to configure additional D, P, and Q parameters. We define a max value for Q and P of 3. Previously we have already seen that further differencing does not improve the Stationarity. Therefore, we can set the value of D to 1.

After configuring the parameters, we next fit the model to the time series. The model will try to find the optimal parameters and choose the model with the least AIC.

# split into train and test
pred_periods = 30
split_number = df_sales['beer'].count() - pred_periods # corresponds to a prediction horizion  of 2,5 years
df_train = pd.DataFrame(df_sales['beer'][:split_number]).rename(columns={'beer':'y_train'})
df_test = pd.DataFrame(df_sales['beer'][split_number:]).rename(columns={'beer':'y_test'})

# auto_arima
model_fit = pm.auto_arima(df_train, test='adf', 
                         max_p=3, max_d=3, max_q=3, 
                         seasonal=True, m=12,
                         max_P=3, max_D=2, max_Q=3,
                         trace=True,
                         error_action='ignore',  
                         suppress_warnings=True, 
                         stepwise=True)

# summarize the model characteristics
print(model_fit.summary())

Performing stepwise search to minimize aic
 ARIMA(2,0,2)(1,1,1)[12] intercept   : AIC=inf, Time=3.89 sec
 ARIMA(0,0,0)(0,1,0)[12] intercept   : AIC=3383.210, Time=0.02 sec
 ARIMA(1,0,0)(1,1,0)[12] intercept   : AIC=3351.655, Time=0.38 sec
 ARIMA(0,0,1)(0,1,1)[12] intercept   : AIC=3364.350, Time=1.09 sec
 ARIMA(0,0,0)(0,1,0)[12]             : AIC=3604.145, Time=0.02 sec
 ARIMA(1,0,0)(0,1,0)[12] intercept   : AIC=3349.908, Time=0.11 sec
 ARIMA(1,0,0)(0,1,1)[12] intercept   : AIC=3351.532, Time=0.29 sec
 ARIMA(1,0,0)(1,1,1)[12] intercept   : AIC=3353.520, Time=1.24 sec
 ARIMA(2,0,0)(0,1,0)[12] intercept   : AIC=3312.656, Time=0.10 sec
 ARIMA(2,0,0)(1,1,0)[12] intercept   : AIC=3314.483, Time=0.57 sec
 ARIMA(2,0,0)(0,1,1)[12] intercept   : AIC=3314.378, Time=0.30 sec
 ARIMA(2,0,0)(1,1,1)[12] intercept   : AIC=3305.552, Time=3.02 sec
 ARIMA(2,0,0)(2,1,1)[12] intercept   : AIC=3291.425, Time=4.19 sec
 ARIMA(2,0,0)(2,1,0)[12] intercept   : AIC=3306.914, Time=3.06 sec
 ARIMA(2,0,0)(3,1,1)[12] intercept   : AIC=3276.501, Time=4.67 sec
 ARIMA(2,0,0)(3,1,0)[12] intercept   : AIC=3282.240, Time=5.24 sec
 ARIMA(2,0,0)(3,1,2)[12] intercept   : AIC=inf, Time=7.39 sec
 ARIMA(2,0,0)(2,1,2)[12] intercept   : AIC=inf, Time=4.74 sec
 ARIMA(1,0,0)(3,1,1)[12] intercept   : AIC=3313.877, Time=5.17 sec
 ARIMA(3,0,0)(3,1,1)[12] intercept   : AIC=3246.820, Time=5.72 sec
 ARIMA(3,0,0)(2,1,1)[12] intercept   : AIC=3255.313, Time=5.33 sec
 ARIMA(3,0,0)(3,1,0)[12] intercept   : AIC=3249.998, Time=6.77 sec
 ARIMA(3,0,0)(3,1,2)[12] intercept   : AIC=inf, Time=8.39 sec
 ARIMA(3,0,0)(2,1,0)[12] intercept   : AIC=3259.938, Time=3.55 sec
...
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).

Auto-ARIMA has determined that the best model is (3,0,0)(3,1,1). These results match the results from section 3, in which we manually performed differencing.

Step #5 Simulate the Time Series using in-sample Forecasting

Now that we have trained our model, we want to use it to simulate the entire time series. We will do this by calling the predict method in the sample function. The prediction will match the same period as the original time series with which we trained the model. Because the model predicts one step, the prediction results will naturally be close to the actual values.

# Generate in-sample Predictions
# The parameter dynamic=False means that the model makes predictions upon the lagged values.
# This means that the model is trained until a point in the time-series and then tries to predict the next value.
pred = model_fit.predict_in_sample(dynamic=False) # works only with auto-arima
df_train['y_train_pred'] = pred

# Calculate the percentage difference
df_train['diff_percent'] = abs((df_train['x_train'] - pred) / df_train['x_train'])* 100

# Print the predicted time-series
fig, ax1 = plt.subplots(figsize=(16, 8))
plt.title("In Sample Sales Prediction", fontsize=14)
sns.lineplot(data=df_train[['x_train', 'y_train_pred']], linewidth=1.0)

# Print percentage prediction errors on a separate axis (ax2)
ax2 = ax1.twinx() 
ax2.set_ylabel('Prediction Errors in %', color='purple', fontsize=14)  
ax2.set_ylim([0, 50])
ax2.bar(height=df_train['diff_percent'][20:], x=df_train.index[20:], width=20, color='purple', label='absolute errors')
plt.legend()
plt.show()

Next, we take a look at the prediction errors.

Step #6 Generate and Visualize a Sales Forecast

Now that we have trained an optimal model, we are ready to generate a sales forecast. First, we specify the number of periods that we want to predict. In addition, we create an index from the number of predictions adjacent to the original time series and continue it (prediction_index).

# Generate prediction for n periods, 
# Predictions start from the last date of the training data
test_pred = model_fit.predict(n_periods=pred_periods, dynamic=False)
df_test['y_test_pred'] = test_pred
df_union = pd.concat([df_train, df_test])
df_union.rename(columns={'beer':'y_test'}, inplace=True)

# Print the predicted time-series
fig, ax = plt.subplots(figsize=(16, 8))
plt.title("Test/Pred Comparison", fontsize=14)
sns.despine();
sns.lineplot(data=df_union[['y_train', 'y_train_pred', 'y_test', 'y_test_pred']], linewidth=1.0, dashes=False, palette='muted')
ax.set_xlim([df_union.index[150],df_union.index.max()])
plt.legend()
plt.show()

As shown above, our model’s forecast continues the seasonal pattern of the beer sales time series. On the one hand, this indicates that US beer sales will continue to rise and, on the other hand, that our model works just fine 🙂

Step #7 Measure the Performance of the Sales Forecasting Model

In this section, we will measure the performance of our ARIMA model. To learn more about this topic, check out this relataly article measuring regression performance.

The previous section’s simulation chart shows a few outliers among the prediction errors. Therefore, we focus our analysis on the percentage errors. Two helpful metrics are the mean absolute error (MAPE) and the mean absolute percentage error (MDAPE).

# Mean Absolute Percentage Error (MAPE)
MAPE = np.mean((np.abs(np.subtract(df_test['y_test'], df_test['y_test_pred'])/ df_test['y_test']))) * 100
print(f'Mean Absolute Percentage Error (MAPE): {np.round(MAPE, 2)} %')

# Median Absolute Percentage Error (MDAPE)
MDAPE = np.median((np.abs(np.subtract(df_test['y_test'], df_test['y_test_pred'])/ df_test['y_test'])) ) * 100
print(f'Median Absolute Percentage Error (MDAPE): {np.round(MDAPE, 2)} %')

Mean Absolute Percentage Error (MAPE): 3.94 %  Median Absolute Percentage Error (MDAPE): 3.49 %

The percent errors show that our ARIMA model achieves a decent predictive performance.

Summary

This Python tutorial has shown how to use SARIMA for sales forecasting. Sales forecasting is important for businesses because it can help them to make informed decisions about production, inventory management, and staffing, among other things. By accurately forecasting sales, businesses can ensure that they have the right amount of product available to meet customer sales, avoid overproduction and excess inventory, and plan for future growth. The use cases presented were forecasting beer sales, and we have used arima to analyze seasonal sales data.

In the first part, we have learned how ARIMA works, what Stationarity is and how to check if a time series is stationary. In the second part, we developed an ARIMA model in Python to create a forecast for US beer sales. For this purpose, we created an in-sample forecast and used Auto-tARIMA to find the optimal parameters for our sales forecasting model.

If you have any questions or suggestions, please let me know in the comments, and I will do my best to answer.

Now that we have learned to use ARIMA to forecast beer sales, you really deserved yourself a beer. Cheers! Image created with Midjourney

" data-image-caption="

Now that we have learned to use ARIMA to forecast beer sales, you really deserved yourself a beer. Cheers! Image created with Midjourney

" data-large-file="https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly2-min.png" src="https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly2-min.png" alt="Now that we have learned to use ARIMA to forecast beer sales, you really deserved yourself a beer. Cheers! Image created with Midjourney" class="wp-image-12603" srcset="https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly2-min.png 506w, https://www.relataly.com/wp-content/uploads/2023/03/brewery-arima-beer-sales-forecasting-python-tutorial-machine-learning-relataly2-min.png 300w" sizes="(max-width: 506px) 100vw, 506px" />

Now that you have learned to use ARIMA to forecast beer sales, you really earned yourself a beer. Cheers! Image created with Midjourney

Sources and Further Reading

The links above to Amazon are affiliate links. By buying through these links, you support the Relataly.com blog and help to cover the hosting costs. Using the links does not affect the price.

Want to learn more about time series analysis and prediction?
Check out these recent relataly tutorials:

The post Forecasting Beer Sales with ARIMA in Python appeared first on relataly.com.

Color-Coded Cryptocurrency Price Charts in Python

Florian Follonier — Tue, 19 Jan 2021 21:03:16 +0000

Are you intrigued by the fascinating world of cryptocurrency and looking to visually decipher its price trends? Welcome aboard! In this comprehensive tutorial, we will explore creating color-coded line charts using Python and Matplotlib, a powerful tool for effective analysis of changes along a third dimension.

The past few years have witnessed a meteoric rise in the prices of cryptocurrencies, underscoring the need for accurate analysis and visualization of their price trends. An outstanding illustration of this is the color-coded Bitcoin stock-to-flow chart, a popular choice in the crypto space that uses color differentiation to denote time until the next Bitcoin halving event.

Drawing inspiration from this, our tutorial will guide you to create a similar dynamic color-coded line chart, tracing the price trends of two leading cryptocurrencies – Bitcoin and Ethereum. This visual aid will provide a deeper insight into their price trajectories over time, enabling you to make informed investment decisions.

As we dive in, we’ll break down the process into digestible chunks, making it easier for beginners to follow along. By the end of this tutorial, you’ll not only have a profound understanding of how to create and interpret such color-coded charts but also gain valuable insights into the world of cryptocurrency price trends.

What are Color-coded Price Charts?

Color coding is beneficial for visualizing trading signals and statistical indicators in technical chart analysis. The idea of color-coding in chart analysis is to create visually comprehensible charts that let the user quickly interpret how price develops under certain conditions. A simple example is a candlestick chart, which uses color to signal whether the price moves up (green) or down (red). Candlestick charts visualize more as regular line charts, providing additional information on the opening and closing prices.

We can use color codings in line plots to visualize conditions of various types. We can derive them from the price itself and, for example, illustrate the price development independence of oscillation indicators or moving averages. Or they can be independent of the price and represent some other conditions, such as, for example, the spread of COVID-19 cases worldwide. These are just a few examples, and there are no limits to your creativity in choosing the conditions.

Also: Requesting Crypto Price Data from the Gate.io REST API in Python

Use Cases for Color-coded Price Charts

There are various use cases for color-coded line plots in the crypto space. For example, crypto enthusiasts employ them to visualize relationships between the price of bitcoin and statistical indicators, including momentum indicators such as the RSI. Color-coded line plots have also been used to show dependencies between price and specific events that develop parallel to the bitcoin price. For example, we can use color-coding to highlight the lag between the price and the bitcoin halving every four years.

The Stock to Flow Model is an example of a Color-coded price chart (Source: lookintobitcoin.com)

Implementing Color-coded Price Charts in Python

Are you ready to elevate your data visualization skills and create visually striking price charts with Python? In this tutorial, we’ll be walking you through the creation of two dynamic line charts that use color to reveal intriguing trends and patterns. The first chart will feature a color overlay on the price line to showcase how Bitcoin prices fluctuate based on RSI. The second chart will unveil the shifting correlation between Bitcoin and Ethereum over time. Buckle up, and let’s dive in!

We’ll start by using the Coinbase Pro API to download historical price data on BTC and ETH. We’ll then calculate two well-established indicators in financial analysis: the Relative Strength Index (RSI) and the Pearson Correlation between Bitcoin and Ethereum. Finally, we’ll use Matplotlib to create stunning color-coded line charts that highlight the changes in the indicators over extended periods.

Also: Geographic Heat Maps with GeoPandas: Visualizing COVID-19

The code is available on the GitHub repository.

View on GitHub Relataly GitHub Repo

Prerequisites

Before starting the coding part, make sure that you have set up your Python 3 environment and required packages. If you don’t have an environment, you can follow this tutorial to set up the Anaconda environment. Also, make sure you install all required packages. In this tutorial, we will be working with the following standard packages:

In addition, we will be using the Historic-Crypto Python Package, which lets us easily interact with the Coinbase Pro API.

You can install packages using console commands:

pip install
conda install (if you are using the anaconda packet manager)

Step #1 Load the Price Data via the Coinbase API

We begin by downloading the historical price data on Bitcoin (BTC-USD) and Ethereum (BTC-USD) from Coinbase Pro. Don’t worry; you don’t need to download the data manually. Instead, we will use the Historic_Crypto Python package to access the data via an API.

Accessing the data via the Coinbase Pro API requires us to specify several API parameters. We define a frequency of 21600 seconds so that we will obtain price points on a 6-hour basis. In addition, we define a from_date of “2017-01-01” and add “ETH-USD” and “BTC-USD” to a list of coins for which we want to obtain the historical price data.

We query the API separately for each of the two coins in our coin list. Depending on your internet connection, this can take several minutes. The response contains three different price values:

high: the daily price high
low: the daily price low
close: the daily closing price

Later in this article, we will require all three variables to calculate the indicator values. We will therefore add the variables as columns to a new dataframe.

# Tested with Python 3.8.8, Matplotlib 3.5, Seaborn 0.11.1, numpy 1.19.5

from Historic_Crypto import HistoricalData
import pandas as pd 
from scipy.stats import pearsonr
import matplotlib.pyplot as plt 
import matplotlib.colors as col 
import numpy as np 
import datetime

# the price frequency in seconds: 21600 = 6 hour price data, 86400 = daily price data
frequency = 21600

# The beginning of the period for which prices will be retrieved
from_date = '2017-01-01-00-00'
# The currency price pairs for which the data will be retrieved
coinlist = ['ETH-USD', 'BTC-USD']

# Query the data
for i in range(len(coinlist)):
    coinname = coinlist[i]
    pricedata = HistoricalData(coinname, frequency, from_date).retrieve_data()
    pricedf = pricedata[['close', 'low', 'high']]
    if i == 0:
        df = pd.DataFrame(pricedf.copy())
    else:
        df = pd.merge(left=df, right=pricedf, how='left', left_index=True, right_index=True)   
    df.rename(columns={"close": "close-" + coinname}, inplace=True)
    df.rename(columns={"low": "low-" + coinname}, inplace=True)
    df.rename(columns={"high": "high-" + coinname}, inplace=True)
df.head()

			time	close-ETH-USD	low-ETH-USD	high-ETH-USD	close-BTC-USD	low-BTC-USD	high-BTC-USD
2017-01-01 06:00:00	8.23			8.16		8.49			975.00			964.54		975.00
2017-01-01 12:00:00	8.33			8.20		8.44			994.42			974.01		994.97
2017-01-01 18:00:00	8.18			8.08		8.37			992.95			986.86		1000.00
2017-01-02 00:00:00	8.13			8.05		8.22			1003.64			990.52		1012.00
2017-01-02 06:00:00	8.10			8.09		8.20			1024.84			1002.92		102

Step #2 Visualizing the Time Series

At this point, we have created a dataframe that contains the price “close,” “low,” and “high” for BTC-USD and ETH-USD. Next, let’s take a quick look at what the data looks like:

# Create a Price Chart on BTC and ETH
x = df.index
fig, ax1 = plt.subplots(figsize=(16, 8), sharex=False)

# Price Chart for BTC-USD Close
color = 'tab:blue'
y = df['close-BTC-USD']
ax1.set_xlabel('time (s)')
ax1.set_ylabel('BTC-Close in $', color=color, fontsize=18)
ax1.plot(x, y, color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax1.text(0.02, 0.95, 'BTC-USD',  transform=ax1.transAxes, color=color, fontsize=16)

# Price Chart for ETH-USD Close
color = 'tab:red'
y = df['close-ETH-USD']
ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
ax2.set_ylabel('ETH-Close in $', color=color, fontsize=18)  # we already handled the x-label with ax1
ax2.plot(x, y, color=color)
ax2.tick_params(axis='y', labelcolor=color)
ax2.text(0.02, 0.9, 'ETH-USD',  transform=ax2.transAxes, color=color, fontsize=16)

Next, we add two indicator values to our dataframe that we can later use to color the price chart.

Step #3 Calculate Indicator Values

The color overlay of the price chart is typically used to illustrate the relation between price and another variable, such as a statistical indicator. To demonstrate how this works, we will calculate two indicators and add them to our dataframe:

3.1 The Relative Strength Index

The Relative Strength Index (RSI) is a momentum indicator that signals the strength of a price trend. Its value range from 0 to 100%. A value above 70% signals that an asset is likely overbought. An overbought level is an area where the market is highly bullish and might decline. A value below 30% is typically a sign of an oversold condition. An oversold level is where the market is extremely bearish, and the price tends to reverse to the upper side.

3.2 The Pearson Correlation Coefficient

Pearson Correlation Coefficient: This indicator measures the correlation between two sets of stochastic variables. Its values range from -1 to 1. A value of 1 would imply a perfect stochastic correlation. For example, if the price of BTC changes by X percentage in a given period, we can expect ETH to experience the exact price change. A value of -1 would imply a perfect inverse correlation. For example, if the price of BTC were to increase by Y percent, we would also expect the ETH price to decrease by Y percent. A value of 0 implies no correlation. To learn more about correlation, check out my article about correlation in Python.

We embed the logic for calculating the two indicators in a different method called “add_indicators.”

def add_indicators(df):
    # Calculate the 30 day Pearson Correlation 
    cor_period = 30 #this corresponds to a monthly correlation period
    columntobeadded = [0] * cor_period
    df = df.fillna(0) 
    for i in range(len(df)-cor_period):
        btc = df['close-BTC-USD'][i:i+cor_period]
        eth = df['close-ETH-USD'][i:i+cor_period]
        corr, _ = pearsonr(btc, eth)
        columntobeadded.append(corr)    
    # insert the colours into our original dataframe    
    df.insert(2, "P_Correlation", columntobeadded, True)

    # Calculate the RSI
    # Moving Averages on high, lows, and std - different periods
    df['MA200_low'] = df['low-BTC-USD'].rolling(window=200).min()
    df['MA14_low'] = df['low-BTC-USD'].rolling(window=14).min()
    df['MA200_high'] = df['high-BTC-USD'].rolling(window=200).max()
    df['MA14_high'] = df['high-BTC-USD'].rolling(window=14).max()

    # Relative Strength Index (RSI)
    df['K-ratio'] = 100*((df['close-BTC-USD'] - df['MA14_low']) / (df['MA14_high'] - df['MA14_low']) )
    df['RSI'] = df['K-ratio'].rolling(window=3).mean() 

    # Replace nas 
    #nareplace = df.at[df.index.max(), 'close-BTC-USD']    
    df.fillna(0, inplace=True)    
    return df
    
dfcr = add_indicators(df)

At this point, we have added the RSI and the Correlation Coefficient to our dataframe. Let’s quickly visualize the two indicators in a line chart.

# Visualize measures
fig, ax1 = plt.subplots(figsize=(22, 4), sharex=False)
plt.ylabel('ETH-BTC Price Correlation', color=color)  # we already handled the x-label with ax1
x = y = dfcr.index
ax1.plot(x, dfcr['P_Correlation'], color='black')
ax2 = ax1.twinx()
ax2.plot(x, dfcr['RSI'], color='blue')
plt.tick_params(axis='y', labelcolor=color)

plt.show()

You may have noticed that the indicators remain at 0 at the time series beginning. However, this is perfectly fine. Since both indicators are calculated retrospectively, no values are available initially.

Step #4 Converting Indicator Values to Color Codes

Before creating the price charts, we have to color code the indicator values. We normalize the values and then assign a color to each indicator value using a color scale. We attach the colors to our existing dataframe to quickly access them when creating the plots.

# function that converts a given set of indicator values to colors
def get_colors(ind, colormap):
    colorlist = []
    norm = col.Normalize(vmin=ind.min(), vmax=ind.max())
    for i in ind:
        colorlist.append(list(colormap(norm(i))))
    return colorlist

# convert the RSI                         
y = np.array(dfcr['RSI'])
colormap = plt.get_cmap('plasma')
dfcr['rsi_colors'] = get_colors(y, colormap)

# convert the Pearson Correlation
y = np.array(dfcr['P_Correlation'])
colormap = plt.get_cmap('plasma')
dfcr['cor_colors'] = get_colors(y, colormap)

In our dataframe, two additional columns contain the color values for the two indicators. Now that we have all the data in our dataframe, the next step is creating the price charts.

Step #5 Creating Color-Coded Price Charts

Next, we use the color values to create two different color-coded price charts.

5.1 Bitcoin Price Chart Colored by RSI

We color the chart with the strength of the correlation between Bitcoin and Ethereum. Light-colored fields signal phases of a strong correlation. Price points colored in dark blue indicate phases where the correlation between the price movements of the two cryptocurrencies was negative.

# Create a Price Chart
pd.plotting.register_matplotlib_converters()
fig, ax1 = plt.subplots(figsize=(18, 10), sharex=False)
x = dfcr.index
y = dfcr['close-BTC-USD']
z = dfcr['rsi_colors']

# draw points
for i in range(len(dfcr)):
    ax1.plot(x[i], np.array(y[i]), 'o',  color=z[i], alpha = 0.5, markersize=5)
ax1.set_ylabel('BTC-Close in $')
ax1.tick_params(axis='y', labelcolor='black')
ax1.set_xlabel('Date')
ax1.text(0.02, 0.95, 'BTC-USD - Colored by RSI',  transform=ax1.transAxes, fontsize=16)

# plot the color bar
pos_neg_clipped = ax2.imshow(list(z), cmap='plasma', vmin=0, vmax=100, interpolation='none')
cb = plt.colorbar(pos_neg_clipped)

From the color overlay in the chart, we can tell that the RSI is low mainly (dark blue) when the Bitcoin price has seen a substantial decline and high (yellow) when the price has risen.

5.2 Bitcoin Price Chart colored by BTC-ETH Correlation

In this section, we will create another price chart for Bitcoin. This time we color code the price trend with the RSI. High RSI values are yellow, and low values are dark blue. Running the code below will create the color-coded bitcoin chart.

# create a price chart
pd.plotting.register_matplotlib_converters()
fig, ax1 = plt.subplots(figsize=(18, 10), sharex=False)
x = dfcr.index # datetime index
y = dfcr['close-BTC-USD'] # the price variable
z = dfcr['cor_colors'] # the color coded indicator values

# draw points
for i in range(len(dfcr)):
    ax1.plot(x[i], np.array(y[i]), 'o',  color=z[i], alpha = 0.5, markersize=5)
ax1.set_ylabel('BTC-Close in $')
ax1.tick_params(axis='y', labelcolor='black')
ax1.set_xlabel('Date')
ax1.text(0.02, 0.95, 'BTC-USD - Colored by 50-day ETH-BTC Correlation',  transform=ax1.transAxes, fontsize=16)

# plot the color bar
pos_neg_clipped = ax2.imshow(list(z), cmap='Spectral', vmin=-1, vmax=1, interpolation='none')
cb = plt.colorbar(pos_neg_clipped)

The chart shows that the correlation between Bitcoin and Ethereum (yellow color) was strong when the price of bitcoin rose. So when Bitcoin is in a bull market, Ethereum tends to follow a similar price logic. In contrast, the correlation was weak when the Bitcoin price declined (dark blue).

Summary

In this article, we demonstrated how to use Python and Seaborn to create a price chart that incorporates color as a third dimension. We used the Bitcoin price as an example and created two color-coded charts: one that highlights the RSI, and another that highlights the Pearson Correlation between Bitcoin and Ethereum.

By using color as an overlay, it is possible to highlight many interesting relationships in time-series data. A well-known example from the cryptocurrency world is the Bitcoin Rainbow Chart. This technique can be used to bring attention to various trends and patterns in the data.

I hope this article has helped to bring you closer to charts in Python. I am always interested to receive feedback from my audience. So, let me know if you liked this content, and if you have any questions, please post them in the comments.

Sources and Further Reading

The links above to Amazon are affiliate links. By buying through these links, you support the Relataly.com blog and help to cover the hosting costs. Using the links does not affect the price.

And if you are interested in stock-market prediction, check out the following articles:

The post Color-Coded Cryptocurrency Price Charts in Python appeared first on relataly.com.