Backtest Manager is a powerful VSCode extension that streamlines algorithmic trading strategy development, making backtesting both simple and efficient.
Developing trading strategies can be complex and time-consuming. Backtest Manager offers:
- Streamlined Development – Direct integration with your IDE and engine lets you focus on strategy logic, eliminating setup complexities.
- Real-time Analysis – Get instant visualization of results and quickly iterate parameters with comprehensive performance metrics.
- AI-Enhanced Workflow – Accelerate development using advanced AI assistants (Copilot, Cursor, Roo, Cline).
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| Strategy Settings | Result Analysis | Dataset Management |
- Strategy & Backtest Management: Build, organize, and keep your backtest projects tidy in VSCode.
- Quick Parameter Tweaks: Change settings and rerun tests easily.
- Visual Analysis: Instantly view results with clear charts and stats.
- Dataset Download: Easily fetch datasets from any exchange and timeframe.
- Backtrader
- VectorBT
- Any Python-based Backtest Engines (choose your favorite)
Coming Soon...
- Nautilus Trader, LEAN Engine (QuantConnect), Language support beyond Python
- Visual Studio Code 1.98.0 or higher
- Python 3.8+
- Backtest libraries (Choose)
# backtrader
pip install backtrader
# vectorBT
pip install vectorbt- Search for and install the Backtest Manager extension in the VSCode marketplace.
- Restart VSCode (recommended).
Backtest Manager provides a basic template, and you can write your own strategies, even let AI write instead of you! Let your trading ideas flow!
Backtrader Example
import backtrader as bt
import os
class SMACrossOverStrategy(bt.Strategy):
params = (
('fastPeriod', int(os.environ['fast'])),
('slowPeriod', int(os.environ['slow'])),
('stopLoss', 2),
('takeProfit', 5)
)
def __init__(self):
self.fast_ma = bt.indicators.SimpleMovingAverage(
self.data.close,
period=self.params.fastPeriod
)
self.slow_ma = bt.indicators.SimpleMovingAverage(
self.data.close,
period=self.params.slowPeriod
)
self.crossover = bt.indicators.CrossOver(self.fast_ma, self.slow_ma)
self.entry_price = None
self.stop_loss = None
self.take_profit = None
def next(self):
if not self.position:
if self.crossover > 0:
close = self.data.close[0]
self.buy()
self.entry_price = close
self.stop_loss = close * (1 - self.params.stopLoss / 100)
self.take_profit = close * (1 + self.params.takeProfit / 100)
else:
close = self.data.close[0]
if close <= self.stop_loss or close >= self.take_profit:
self.close()
self.entry_price = None
self.stop_loss = None
self.take_profit = None
return
if self.crossover < 0:
self.close()
self.entry_price = None
self.stop_loss = None
self.take_profit = None
def cerebro_init(cerebro: bt.Cerebro):
cerebro.addsizer(bt.sizers.PercentSizer, percents=95)
# No driver code (e.g. cerebro.run()) needed
# Backtrader Documentation: https://www.backtrader.com/docu/quickstart/quickstart/Each project must include a cerebro_init function. This lets the extension handle data loading and configuration automatically before running your backtest.
VectorBT Example
import vectorbt as vbt
# Build portfolio args
def portfolio_init(close) -> dict:
portfolio_kwargs = {}
fast_ma = vbt.MA.run(close, 10)
slow_ma = vbt.MA.run(close, 50)
portfolio_kwargs['close'] = close
portfolio_kwargs['entries'] = fast_ma.ma_crossed_above(slow_ma)
portfolio_kwargs['exits'] = fast_ma.ma_crossed_below(slow_ma)
return portfolio_kwargs
# No driver code (e.g. vbt.Portfolio.from_signals()) needed
# VectorBT Documentation: https://vectorbt.dev/Each project must include a portfolio_init function. The extension uses this to load data, set parameters, and run your backtest automatically.
Custom Engine Example
# Write backtesting code with your custom engine here.
# Don't forget to print your backtest results so that Backtest Manager can capture them.
print("t: {
'ref': 0,
'datetime': "2024-05-30T14:00:00+00:00",
'price': 10202.0,
'size': 2,
'value': 20404.0,
'commission': 20.4,
'side': 'long'
}")
print("t: {
'ref': 0,
'datetime': "2024-05-31T00:00:00+00:00",
'price': 10302,
'size': 2,
'pnl': 200.0,
'pnlcomm': 179.6,
'commission': 20.4,
'hold_bars': 10
}")
print("e: {
'datetime': "2024-05-30T14:00:00+00:00",
'value': 3000000
}")Start a new backtest project. Choose your favourite backtest engine.
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| Create New Project |
Easily download historical data for your backtests with just a few clicks.
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| Dataset Organization | Dataset Download Interface |
Ready, Set, Go!
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| Parameter Configuration |
Analyze your strategy's performance with comprehensive analytics:
- Equity curve graph – visualize your profits
- Performance metrics (total return, Sharpe ratio, max drawdown, win rate, etc.)
- Trade history (time, price, size, profit, etc.)
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| Performance Analytics |
You can enable verbose output for your backtest scripts and choose whether to automatically delete completed backtest scripts in the extension settings.
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| Debug Settings |
Distributed under the MIT License.
Have ideas to make this even better? Issues and pull requests are always welcome! Please check the issue tracker before contributing.