Algorithmic Trading Model using Weekly MACD and RSI Indicators

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model employs a simple mean-reversion strategy using the Moving Average Convergence/Divergence Oscillator (MACD) and Relative Strength Index (RSI) for the entry and exit signals. For the MACD trend indicators, the model will use 12-week and 26-week periods for the MACD line. For the MACD signal line, we will use a 9-week EMA of the MACD line. Finally, the RSI will operate using a 14-week look-back period.

The model will signal for a long position when the MACD histogram switches from negative to positive, assuming the RSI signal is below the overbought threshold. Conversely, the model will look to initiate a short position when the signals reverse themselves.

We will compare two trading models with different buying approaches. The first model will use a long-only method, while the second model will take long and short positions. Both models will use a 10% stop-loss limit for each trade.

ANALYSIS: In this modeling iteration, we analyzed ten stocks between October 1, 2011, and October 1, 2021. The models’ performance appeared at the end of the script. The models with the long-only approach produced a better return than the long-short method. Moreover, the simple buy-and-hold system came out ahead for all stocks.

CONCLUSION: For most stocks during the modeling time frame, the customized trading strategy with the MACD and RSI signals did not produce a better return than the buy-and-hold approach. We should consider modeling these stocks further by experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model using Daily MACD and RSI Indicators

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model employs a simple mean-reversion strategy using the Moving Average Convergence/Divergence Oscillator (MACD) and Relative Strength Index (RSI) for the entry and exit signals. For the MACD trend indicators, the model will use 12-day and 26-day periods for the MACD line. For the MACD signal line, we will use a 9-day EMA of the MACD line. The RSI will operate using a 14-day look-back period.

The model will signal for a long position when the MACD histogram switches from negative to positive, assuming the RSI signal is below the overbought threshold. Conversely, the model will look to initiate a short position when the signals reverse themselves.

We will compare two trading models with different buying approaches. The first model will use a long-only method, while the second model will take long and short positions. Both models will use a 10% stop-loss limit for each trade.

ANALYSIS: In this modeling iteration, we analyzed ten stocks between October 1, 2011, and October 1, 2021. The models’ performance appeared at the end of the script. The models with the wider signal line width generally produced a better return for the tested stocks. Moreover, the simple buy-and-hold approach came out ahead for all stocks.

CONCLUSION: For most stocks during the modeling time frame, the customized trading strategy with the MACD and RSI signals did not produce a better return than the buy-and-hold approach. We should consider modeling these stocks further by experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model using Stochastic RSI with Different Signal Levels

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model employs a simple mean-reversion strategy using the Stochastic RSI (StochRSI) indicators for stock position entries and exits. For the Stochastic RSI indicator, the model will use a 14 look-back period. The model will initiate a long position when the indicator crosses the lower signal line from above. Conversely, the model will exit the long position when the indicator crosses the upper signal line from below.

ANALYSIS: In this modeling iteration, we analyzed ten stocks between August 1, 2016, and September 10, 2021. The models’ performance appeared at the end of the script. The models with the wider signal line width generally produced a better return for the tested stocks. Moreover, the simple buy-and-hold approach came out ahead for all stocks.

CONCLUSION: For most stocks during the modeling time frame, the long-only trading strategy with the Stochastic RSI signals did not produce a better return than the buy-and-hold approach. We should consider modeling these stocks further by experimenting with more variations of the strategy.

CONCLUSION: For most stocks during the modeling time frame, the long-only trading strategy with the Stochastic Oscillator signals did not produce a better return than the buy-and-hold approach. We should consider modeling these stocks further by experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model for Mean-Reversion with RSI Exit and Stop Loss Signals for a Group of Stocks Using Python

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model compares a simple mean-reversion strategy with or without using RSI as the exit signal for an individual stock. The model will use a trend window size of ten days for long trades only. When the 14-day RSI value reaches 70, the model will exit the long position. In addition, the model will also generate an exit signal when the holding reaches a 10% loss at the end of the day.

ANALYSIS: In this modeling iteration, we analyzed 15 stocks between January 1, 2016, and July 30, 2021. The models’ performance appeared at the end of the script.

CONCLUSION: For all the stocks during the modeling time frame, the long-only trading strategy with or without RSI as the exit signal did not produce a better return than the buy-and-hold approach, except for F. We should consider experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model for Mean-Reversion with RSI Exit and Stop Loss Signals for an Individual Stock Using Python

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model compares a simple mean-reversion strategy with or without using RSI as the exit signal for an individual stock. The model will use a trend window size of ten days for long trades only. When the 14-day RSI value reaches 70, the model will exit the long position. In addition, the model will also generate an exit signal when the holding reaches a 10% loss at the end of the day.

ANALYSIS: In this modeling iteration, we analyzed the stock of AAPL (Apple Inc.) between January 1, 2016, and July 30, 2021. The mean-reversion model without using RSI produced a gain of 26.90% per share, while the model with RSI signals returned a profit of 121.95%. In addition, the buy-and-hold approach yielded an increase of 462.84% per share.

CONCLUSION: For the AAPL stock during the modeling time frame, the long-only trading strategy with or without RSI as the exit signal did not produce a better return than the buy-and-hold approach. We should consider experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model for Trend-Following with RSI Exit Signal for a Group of Stocks Using Python

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model compares a simple trend-following strategy with or without using RSI as the exit signal for an individual stock. The model will use a trend window size of ten days for long trades only. When the 14-day RSI value reaches 70, the model will exit the long position.

ANALYSIS: In this modeling iteration, we analyzed 14 stocks between January 1, 2016, and July 26, 2021. The models’ performance appeared at the end of the script.

CONCLUSION: For all the stocks during the modeling time frame, the long-only trading strategy with or without RSI as the exit signal did not produce a better return than the buy-and-hold approach, except for Citigroup (C), Southwest Airlines (LUV), and Wells Fargo Bank (WFC). We should consider modeling these stocks further by experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model for Trend-Following with RSI Exit Signal for an Individual Stock Using Python

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model compares a simple trend-following strategy with or without using RSI as the exit signal for an individual stock. The model will use a trend window size of ten days for long trades only. When the 14-day RSI value reaches 70, the model will exit the long position.

ANALYSIS: In this modeling iteration, we analyzed the stock of AAPL (Apple Inc.) between January 1, 2016, and July 26, 2021. The mean-reversion model without using RSI produced a profit of 51.55 dollars per share, while the model with RSI signals returned 90.01. In addition, the buy-and-hold approach yielded a gain of 122.61 dollars per share.

CONCLUSION: For the AAPL stock during the modeling time frame, the long-only trading strategy with or without RSI as the exit signal did not produce a better return than the buy-and-hold approach. We should consider experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.

Algorithmic Trading Model for Mean-Reversion with RSI Exit Signal for a Group of Stocks Using Python

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model compares a simple mean-reversion strategy with or without using RSI as the exit signal for an individual stock. The model will use a trend window size of ten days for long trades only. When the 14-day RSI value reaches 70, the model will exit the long position.

ANALYSIS: In this modeling iteration, we analyzed 14 stocks between January 1, 2016, and July 16, 2021. The models’ performance appeared at the end of the script.

CONCLUSION: For all the stocks during the modeling time frame, the long-only trading strategy with or without RSI as the exit signal did not produce a better return than the buy-and-hold approach, except for NFLX. We should consider modeling these stocks further by experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.