trading strategy is a fixed plan to go long or short in markets, there are two common trading strategies: the momentum strategy and the reversion strategy. Firstly, the momentum strategy is also called divergence or trend trading. When you follow this strategy, you do so because you believe the movement of a quantity will continue in its current direction. Stated differently, you believe that stocks have momentum or upward or downward trends, that you can detect and exploit. Some examples of this strategy are the moving average crossover, the dual moving average crossover, and turtle trading: The moving average crossover is when the price of an asset moves from one side of a moving average to the other. This crossover represents a change in momentum and can be used as a point of making the decision to enter or exit the market. You’ll see an example of this strategy, which is the “hello world” of quantitative trading later on in this tutorial. The dual moving average crossover occurs when a short-term average crosses a long-term average. This signal is used to identify that momentum is shifting in the direction of the short-term average. A buy signal is generated when the short-term average crosses the long-term average and rises above it, while a sell signal is triggered by a short-term average crossing long-term average and falling below it. Turtle trading is a popular trend following strategy that was initially taught by Richard Dennis. The basic strategy is to buy futures on a 20-day high and sell on a 20-day low. Secondly, the reversion strategy, which is also known as convergence or cycle trading. This strategy departs from the belief that the movement of a quantity will eventually reverse. This might seem a little bit abstract, but will not be so anymore when you take the example. Take a look at the mean reversion strategy, where you actually believe that stocks return to their mean and that you can exploit when it deviates from that mean. That already sounds a whole lot more practical, right? Another example of this strategy, besides the mean reversion strategy, is the pairs trading mean-reversion, which is similar to the mean reversion strategy. Whereas the mean reversion strategy basically stated that stocks return to their mean, the pairs trading strategy extends this and states that if two stocks can be identified that have a relatively high correlation, the change in the difference in price between the two stocks can be used to signal trading events if one of the two moves out of correlation with the other. That means that if the correlation between two stocks has decreased, the stock with the higher price can be considered to be in a short position. It should be sold because the higher-priced stock will return to the mean. The lower-priced stock, on the other hand, will be in a long position because the price will rise as the correlation will return to normal. Besides these two most frequent strategies, there are also other ones that you might come across once in a while, such as the forecasting strategy, which attempts to predict the direction or value of a stock, in this case, in subsequent future time periods based on certain historical factors. There’s also the High-Frequency Trading (HFT) strategy, which exploits the sub-millisecond market microstructure. That’s all music for the future for now; Let’s focus on developing your first trading strategy for now! A Simple Trading Strategy As you read above, you’ll start with the “hello world” of quantitative trading: the moving average crossover. The strategy that you’ll be developing is simple: you create two separate Simple Moving Averages (SMA) of a time series with differing lookback periods, let’s say, 40 days and 100 days. If the short moving average exceeds the long moving average then you go long, if the long moving average exceeds the short moving average then you exit. Remember that when you go long, you think that the stock price will go up and will sell at a higher price in the future (= buy signal); When you go short, you sell your stock, expecting that you can buy it back at a lower price and realize a profit (= sell signal). This simple strategy might seem quite complex when you’re just starting out, but let’s take this step by step: First define your two different lookback periods: a short window and a long window. You set up two variables and assign one integer per variable. Make sure that the integer that you assign to the short window is shorter than the integer that you assign to the long window variable! Next, make an empty signals DataFrame, but do make sure to copy the index of your aapl data so that you can start calculating the daily buy or sell signal for your aapl data. Create a column in your empty signals DataFrame that is named signal and initialize it by setting the value for all rows in this column to 0.0. After the preparatory work, it’s time to create the set of short and long simple moving averages over the respective long and short time windows. Make use of the rolling() function to start your rolling window calculations: within the function, specify the window and the min_period, and set the center argument. In practice, this will result in a rolling() function to which you have passed either short_window or long_window, 1 as the minimum number of observations in the window that are required to have a value, and False, so that the labels are not set at the center of the window. Next, don’t forget to also chain the mean() function so that you calculate the rolling mean. After you have calculated the mean average of the short and long windows, you should create a signal when the short moving average crosses the long moving average, but only for the period greater than the shortest moving average window. In Python, this will result in a condition: signals['short_mavg'][short_window:] > signals['long_mavg'][short_window:]. Note that you add the [short_window:] to comply with the condition “only for the period greater than the shortest moving average window”. When the condition is true, the initialized value 0.0 in the signal column will be overwritten with 1.0. A “signal” is created! If the condition is false, the original value of 0.0 will be kept and no signal is generated. You use the NumPy where() function to set up this condition. Much the same like you read just now, the variable to which you assign this result is signals['signal'][short_window], because you only want to create signals for the period greater than the shortest moving average window! Lastly, you take the difference of the signals in order to generate actual trading orders. In other words, in this column of your signals DataFrame, you’ll be able to distinguish between long and short positions, whether you’re buying or selling stock.

stock-pairs-trading is a python library for backtest with stock pairs trading using kalman filter on Python 3.8 and above.

To create a data-web application deployed using the azure app service, which was made on Streamlit, the leading Pythonic data application service. On this website, we display candlestick plots of various stocks listed on the Nasdac, according to the option of the user; and utilize the Garch based time forecasting algorithm done using Seasonal arima model and conduct a virtual future prediction for the given stock, so as to be able to conduct non-pairs algorithmic trading using time forecasting and Garch-based deep learning.

jquants-pairs-trading is a python library for backtest with japanese stock pairs trading using kalman filter, J-Quants on Python 3.8 and above.

This project implements a statistical arbitrage (pairs trading) strategy using historical stock price data (from yfinance). It demonstrates knowledge in quantitative modelling, financial data handling, Python programming, and statistical testing.

Pair trading strategy app using Python & Streamlit. Detects cointegrated stock pairs, generates trading signals via z-score, and includes price charts, signal table, CSV export, and backtesting with Sharpe Ratio & returns.

contains implementations of popular trading strategies such as Fama-French and pairs trading. Includes Python code for simulating stock prices, backtesting the strategies, and visualizing the results

Brazilian Stock Market Analysis Tool: Python-based dashboard that extracts B3 stock data from TradingView and Yahoo Finance, featuring pair trading analysis with correlation, beta calculation, and cointegration testing for identifying trading opportunities in the Brazilian market.

Mostly pair trading on the stock market in Python / Jupyter

A Python implementation of a market-neutral pairs trading strategy using z-score based signals on highly correlated stock pairs.

A Python-based market analysis tool for pair trading that identifies correlated stock pairs using real-time financial data.

Quantitative pairs trading algorithm that scans 10 correlated stock pairs daily and generates statistical arbitrage signals using z-score analysis. Built in Python.

Volatility-Driven Pairs Trading Strategy: AAPL vs. MSFT | Python Data Analysis Project A complete data pipeline from raw stock data to backtested trading signals

"Pair Trading Clustering Strategy: Python code for clustering-based pair trading strategy on stock returns using K-Means, DBSCAN, Agglomerative clustering, retrieving historical data via Yahoo Finance API."

Developing a pairs trading algorithm in Python that identifies price differences between highly correlated and cointegrated stocks in order to short the overperforming stock and long the underperforming stock

Quantitative pairs trading backtester built with Python and Streamlit — implements z-score mean reversion strategy with Sharpe ratio, max drawdown, and cumulative return metrics across any stock pair

A fully functional pairs trading engine built in Google Colab using Python, Pandas, and yFinance. It identifies cointegrated stock pairs and backtests a spread-based long-short strategy with z-score based entry/exit signals.

A statistical arbitrage (Pairs Trading) strategy for the Indian stock market using Python. It implements rolling cointegration and Z-score signals to trade highly correlated NSE assets like HDFC Bank and ICICI Bank.

Statistical arbitrage bot using cointegration-based pairs trading. Screens 2000+ U.S. stock pairs and backtests trades on NXPI–AMAT using z-score signals. ~35% CAGR, Sharpe 1.8, 24% YTD paper returns. Built with Python, yfinance, statsmodels, and matplotlib.

A quantitative pairs trading strategy built in Python — using cointegration analysis, rolling OLS hedge ratios, and Z-score signal generation to backtest a market-neutral mean-reversion strategy on real stock data.

A Python implementation of a cointegration-based pairs trading strategy. This project uses the Engle-Granger method & ADF test to identify a statistically significant stock pair and backtests a market-neutral trading strategy. It capitalizes on mean reversion, executing trades when the price spread's z-score deviates beyond a ±1.25 threshold

We developed a pair trading strategy using python in google collab Following things were done in this project: Used Cointegration analysis and ADF Test for Stock pair selection Developed and implemented Trading Strategy Using Z-Scores Optimized Entry and Exit Points to maximize profits for more details refer to docx. file

I developed a Python-based trading bot that traded bonds, stocks, and ETFs using diverse strategies, including pennying and leveraging the price of a more liquid stock to estimate the fair value of a less liquid stock in an ADR pair. The bot managed to place 2nd out of 16 competing teams, totalling a profit of $3,394,715 across 12 trading rounds.