Quantitative Trading Strategy Backtest

This project develops and tests trading alphas for the Indian and US equity markets, focusing on medium to high-frequency trading strategies. The objective is to create, backtest, and optimize these alphas to enhance trading performance.

Project Overview

Features

Develop trading strategies based on given alphas
Backtest strategies using historical market data
Optimize trading thresholds to maximize profit and loss (PnL)
Visualize the performance of the trading strategies

Getting Started

Prerequisites

Python 3.x
Pandas
Numpy
Matplotlib

Installation

Clone the repository:

git clone https://github.com/saurabh4269/alpha.git
cd alpha

Install the required packages:
```
pip install pandas numpy matplotlib
```
Download the dataset from the provided link and place it in the project directory:
- Dataset
- Rename the downloaded file to asset_1.csv and place it in the project directory.

Usage

Run the script:
```
python backtest.py
```
The script will:
- Load the dataset
- Optimize the trading thresholds
- Apply the optimal strategy
- Backtest the strategy
- Save the results to backtest_results.csv
- Visualize the performance

Script Overview

Classes

`TradingStrategy`

Initialization: Accepts build and liquidate thresholds.
Method: apply_strategy(data) applies the trading strategy to the dataset.

`BacktestEngine`

Initialization: Accepts a strategy and dataset.
Method: run_backtest() applies the strategy, calculates returns and PnL, and generates a cumulative PnL.

Functions

`optimize_thresholds(data)`

Optimizes the build and liquidate thresholds to maximize PnL.

`visualize_backtest_results(data)`

Visualizes the asset price, position over time, and cumulative PnL.

Script Execution

The script first optimizes the thresholds.
It then applies the optimal strategy and runs the backtest.
Finally, it visualizes the results and saves them to a CSV file.

Visualization

Below are example plots generated by the script:

saurabh4269 / alpha