{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview\n", "This project focuses on developing a reinforcement learning-based algorithmic trading strategy with the goal of creating a trading agent that learns optimal trading strategies by interacting with historical market data and making buy/sell decisions based on current market conditions. The reinforcement learning-based trading strategy learns to make trading decisions based on historical market data. The Q-learning algorithm is used to learn the optimal actions (buy, hold, or sell) for different states of the market. The agent is trained through iterative episodes, where it explores and exploits different actions and receives rewards based on the profitability of its decisions.\n", "\n", "The performance of the reinforcement learning strategy is compared to a baseline buy-and-hold strategy. The results show that the reinforcement learning strategy outperforms the buy-and-hold strategy in terms of cumulative returns and Sharpe ratio. However, it also experiences a higher maximum drawdown, indicating potential risks. Overall, the project demonstrates the application of reinforcement learning techniques in developing algorithmic trading strategies. It highlights the potential of using machine learning to learn optimal trading strategies from historical data. However, it also acknowledges the complexities and computational requirements associated with reinforcement learning algorithms.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 1\n", "Collect historical market data for a set of assets (e.g., stocks, cryptocurrencies). Preprocess the data to remove outliers, handle missing values, and format it into a suitable input for the reinforcement learning model." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | AMZN | \n", "AXP | \n", "AMGN | \n", "AAPL | \n", "BA | \n", "CAT | \n", "CSCO | \n", "CVX | \n", "GS | \n", "HD | \n", "... | \n", "NKE | \n", "PG | \n", "TRV | \n", "UNH | \n", "CRM | \n", "VZ | \n", "V | \n", "WMT | \n", "DIS | \n", "DOW | \n", "
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Date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
2022-09-29 00:00:00-04:00 | \n", "114.800003 | \n", "134.829849 | \n", "217.547775 | \n", "141.273102 | \n", "125.330002 | \n", "160.746567 | \n", "38.699997 | \n", "136.735626 | \n", "283.136993 | \n", "267.565887 | \n", "... | \n", "93.491486 | \n", "123.847023 | \n", "149.782745 | \n", "497.780243 | \n", "146.809998 | \n", "34.812500 | \n", "177.911240 | \n", "43.257965 | \n", "97.133430 | \n", "40.799973 | \n", "
2022-09-30 00:00:00-04:00 | \n", "113.000000 | \n", "132.011749 | \n", "214.680923 | \n", "137.029358 | \n", "121.080002 | \n", "158.983109 | \n", "38.156269 | \n", "135.696655 | \n", "280.211060 | \n", "265.268372 | \n", "... | \n", "81.516960 | \n", "121.489410 | \n", "148.349609 | \n", "494.072540 | \n", "143.839996 | \n", "34.208866 | \n", "175.530014 | \n", "42.423885 | \n", "94.023567 | \n", "40.587479 | \n", "
2022-10-03 00:00:00-04:00 | \n", "115.879997 | \n", "137.011948 | \n", "219.481247 | \n", "141.243378 | \n", "126.050003 | \n", "165.910980 | \n", "39.386806 | \n", "143.309326 | \n", "286.043854 | \n", "272.728302 | \n", "... | \n", "83.752991 | \n", "123.664177 | \n", "152.097092 | \n", "504.315186 | \n", "147.899994 | \n", "35.280991 | \n", "179.482269 | \n", "43.349556 | \n", "96.814468 | \n", "41.834751 | \n", "
2022-10-04 00:00:00-04:00 | \n", "121.089996 | \n", "142.335068 | \n", "221.938553 | \n", "144.862442 | \n", "133.509995 | \n", "174.050064 | \n", "40.262924 | \n", "148.881912 | \n", "301.075134 | \n", "278.361664 | \n", "... | \n", "86.930504 | \n", "125.194252 | \n", "156.357788 | \n", "511.808838 | \n", "155.729996 | \n", "35.866600 | \n", "183.434525 | \n", "43.912155 | \n", "101.110474 | \n", "43.072800 | \n", "
2022-10-05 00:00:00-04:00 | \n", "120.949997 | \n", "141.268494 | \n", "222.700500 | \n", "145.159912 | \n", "132.110001 | \n", "172.838882 | \n", "40.426598 | \n", "149.731979 | \n", "295.462280 | \n", "278.640442 | \n", "... | \n", "89.343056 | \n", "124.328163 | \n", "155.457230 | \n", "515.624146 | \n", "156.229996 | \n", "35.497215 | \n", "185.430420 | \n", "43.477119 | \n", "100.472549 | \n", "42.555408 | \n", "
5 rows × 30 columns
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