quant-trading-backtrader

Build, backtest, and optimize quantitative trading strategies in Python using Backtrader with support for indicators, risk management, and reporting.

Security Scan

The skill's code and instructions are largely consistent with a Backtrader backtesting template, but small incoherences (notably an npm package.json listing Python packages and an unknown source) leave uncertainty and warrant caution before installing or running.

Purpose & Capability

Name, description, SKILL.md, and the included example (sma_crossover.py) all align: they implement/backtest a simple SMA crossover using Backtrader, demonstrate stop-loss handling, generate synthetic CSV data, run a backtest, and delete the temp file. There are no requested env vars, binaries, or config paths that conflict with the trading/backtesting purpose.

Instruction Scope

Runtime instructions are narrowly scoped to installing Backtrader/matplotlib and using the provided templates/examples. The SKILL.md and example script only read/write a local temporary CSV, run backtests, and print logs. There are no instructions to read unrelated system files, access network endpoints, or exfiltrate data.

Install Mechanism

The skill is instruction-only (no install spec), which is low risk, but there's a package.json in the bundle listing Python packages (backtrader, matplotlib) as npm dependencies — an incoherence. This manifest is out of place (npm manifests normally list JS packages) and could indicate sloppy packaging or automated conversion; it does not itself install anything, but it is unexpected and should be verified.

Credentials

The skill requests no credentials or environment variables. The example script writes a temporary CSV (temp_data.csv) to the current directory and then removes it — file I/O is limited and proportional to its stated purpose. No sensitive environment access or unrelated credentials are requested.

Persistence & Privilege

The skill does not request persistent or elevated privileges. always is false, and there are no install hooks or configuration changes in the repository. The skill does not attempt to modify other skills or system-wide agent settings.

What to consider before installing

This package behaves like a simple Backtrader example and contains no obvious exfiltration or secret access, but proceed cautiously because the source is unknown and the bundle contains a suspicious npm-style package.json that lists Python packages (likely a packaging mistake). Before running: 1) Verify the publisher or obtain the SKILL from a trusted source. 2) Inspect code yourself (you already have examples/sma_crossover.py) and search for any network calls or subprocess execution. 3) Run in an isolated environment (virtualenv or VM) and avoid running pip install globally — malicious or typo-squatted PyPI packages are a general risk. 4) If you plan to install dependencies, pin known-good package versions from trusted indexes, and consider auditing the actual PyPI packages (or use a vetted wheel). If you need higher assurance, request the upstream source/repository or a maintained release rather than this anonymous bundle.

A comprehensive skill for building, backtesting, and optimizing quantitative trading strategies using the Backtrader framework in Python.

Features

• Backtesting Engine: Simulates trading strategies on historical data with support for multiple data feeds.
• Strategy Development: Provides a structured Strategy class to define indicators (SMA, EMA, RSI, etc.) and trading logic.
• Risk Management: Examples of implementing stop-loss, take-profit, and position sizing (e.g., fractional Kelly).
• Data Handling: Support for CSV data ingestion (customizable formats) and pandas DataFrame integration.
• Reporting: Generates transaction logs, trade analysis (PNL), and portfolio value tracking.

Usage

This skill provides a foundation for creating quantitative trading bots. It includes templates and examples to get you started.

1. Installation

Ensure you have the required dependencies:

pip install backtrader matplotlib

2. Basic Strategy Template

Create a new strategy file (e.g., my_strategy.py) using the template structure:

import backtrader as bt

class MyStrategy(bt.Strategy):
    params = (
        ('period', 15),
    )

    def __init__(self):
        self.sma = bt.indicators.SimpleMovingAverage(self.data.close, period=self.params.period)

    def next(self):
        if self.sma > self.data.close:
            # Do something
            pass

3. Running a Backtest

Use bt.Cerebro to orchestrate the backtest:

cerebro = bt.Cerebro()
cerebro.addstrategy(MyStrategy)
# ... add data ...
cerebro.run()

Examples

Check the examples/ directory for full working examples:

• sma_crossover.py: A classic Trend Following strategy with Stop-Loss.

Best Practices

• Avoid Overfitting: Use Walk-Forward Analysis (train on past, test on unseen future data).
• Risk Control: Always implement stop-loss orders. Position sizing is critical for survival.
• Data Quality: Ensure your historical data is clean and representative.