A backtesting and performance analysis package
Project description
DeltaU
DeltaU is a backtesting and performance analysis package for quantitative trading strategies.
Installation
You can install DeltaU using `pip install DeltaU':
Usage
Example usage:
import pandas as pd
from deltau.core.backtester import Backtester, Executor
from deltau.visualization.tear_sheet import TearSheet
# Create a DataFrame
data = pd.read_csv('stock_data.csv')
# Create a strategey and generate signals
class ExampleStrategy:
def generate_signals(self, data):
# Calculate short and long moving averages
data['Short_MA'] = data['Close'].rolling(window=5).mean()
data['Long_MA'] = data['Close'].rolling(window=20).mean()
# Generate signals: 1 for Buy, -1 for Sell, 0 for Hold
signals = (data['Short_MA'] > data['Long_MA']).astype(int).diff()
return signals
# Instantiate the strategy, executor, and backtester
strategy = ExampleStrategy(window=self.window)
executor = Executor(initial_capital=600, share_size=10)
backtester = Backtester(strategy=strategy, executor=executor, data=data)
# Run the backtest
portfolio_values, final_metrics = backtester.run()
# Create a TearSheet object with the results and metrics
tear_sheet = TearSheet(backtest_results=portfolio_values, metrics=final_metrics)
# Generate the report and show plots
tear_sheet.display_report()
## In case you get an error when calling tear_sheet function while using jupyter
Copy and paste: pip install nbformat>=4.2.0 into your envrionment, restart the environment and than re-run the code.
## DeltaU mainly utilizes dataframes for trade simulation, each must contain Date, Open, High, Low, Close, Adj Close, Volume.
# I've tried my best to make things as simple as calling strategy = ExampleStrategy(window=self.window) executor = Executor(initial_capital=600, share_size=10) backtester = Backtester(strategy=strategy, executor=executor, data=data)>
# In order for the simulation to run successfully each strategy needs to generate a 1 (buy), -1 (sell), or 0 (hold) signal.
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