backtrader

Q: How do I install backtrader?

Run npx skills add https://github.com/agiprolabs/claude-trading-skills --skill backtrader in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/backtrader/ (personal) or .claude/skills/backtrader/ (project).

by agiprolabsBusiness & Finance

Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators

Installs: 504
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/agiprolabs/claude-trading-skills --skill backtrader

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads backtrader automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Backtrader

Backtrader is a Python event-driven backtesting framework that processes data bar-by-bar, simulating realistic execution with a built-in broker, order management, and position tracking. Unlike vectorized frameworks (vectorbt, pandas), backtrader walks through history one bar at a time, firing callbacks that let you implement complex order logic that depends on previous fills, partial executions, and conditional brackets.

Event-Driven vs Vectorized

Aspect	Backtrader (event-driven)	vectorbt (vectorized)
Execution model	Bar-by-bar callbacks	Whole-array operations
Speed	Slower (Python loop)	Fast (NumPy/Numba)
Order types	Market, limit, stop, stop-limit, bracket, OCO	Market only (native)
Realism	Built-in broker with commission, slippage, margin	Manual slippage modeling
Multi-timeframe	Native resampledata	Manual alignment
Best for	Complex strategies, bracket orders, portfolio	Fast parameter sweeps, simple signals

Use backtrader when you need:

Bracket orders (entry + stop loss + take profit as a unit)
Stop-limit or trailing stop orders
Order-dependent logic (scale in after first fill, cancel if not filled in N bars)
Multi-timeframe strategies (daily signals, hourly execution)
Realistic commission and slippage modeling

Use vectorbt when you need:

Fast parameter optimization over thousands of combinations
Simple long/short signals without complex order management
Quick prototyping and statistical analysis of results

Core Concepts

Backtrader has five core objects that interact through an event loop:

1. Cerebro (the engine)

The central orchestrator. You add strategies, data feeds, analyzers, and sizers to Cerebro, then call run().

import backtrader as bt

cerebro = bt.Cerebro()
cerebro.addstrategy(MyStrategy, fast_period=10, slow_period=30)
cerebro.adddata(data_feed)
cerebro.broker.setcash(100_000)
cerebro.broker.setcommission(commission=0.003)  # 0.3%
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe")
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
cerebro.run()

2. Strategy (your logic)

A Strategy subclass contains all trading logic. Key methods:

__init__() — Define indicators. Runs once before backtesting starts.
next() — Called on every bar. Place orders here.
notify_order(order) — Called when order status changes (submitted, accepted, completed, canceled, margin, expired).
notify_trade(trade) — Called when a trade opens or closes. Access P&L here.

class EMACrossover(bt.Strategy):
    params = (
        ("fast_period", 10),
        ("slow_period", 30),
    )

    def __init__(self) -> None:
        self.ema_fast = bt.ind.EMA(period=self.p.fast_period)
        self.ema_slow = bt.ind.EMA(period=self.p.slow_period)
        self.crossover = bt.ind.CrossOver(self.ema_fast, self.ema_slow)

    def next(self) -> None:
        if not self.position:
            if self.crossover > 0:
                self.buy()
        elif self.crossover < 0:
            self.close()

3. Data Feed

Backtrader data feeds provide OHLCV lines. The most common approach is loading from a pandas DataFrame:

import pandas as pd

df = pd.DataFrame({
    "open": [...], "high": [...], "low": [...],
    "close": [...], "volume": [...],
}, index=pd.DatetimeIndex([...]))

data = bt.feeds.PandasData(dataname=df)
cerebro.adddata(data)

For CSV files:

data = bt.feeds.GenericCSVData(
    dataname="ohlcv.csv",
    dtformat="%Y-%m-%d",
    openinterest=-1,  # no open interest column
)

4. Broker

The built-in broker simulates order execution with configurable cash, commission, and slippage.

cerebro.broker.setcash(100_000)
cerebro.broker.setcommission(commission=0.003)  # 0.3% per trade

# Cheat-on-open: execute at the open of the signal bar (avoids lookahead)
cerebro.broker.set_coo(True)

5. Analyzers

Analyzers compute performance metrics after the backtest completes.

cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe",
                    riskfreerate=0.0, annualize=True, timeframe=bt.TimeFrame.Days)
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
cerebro.addanalyzer(bt.analyzers.Returns, _name="returns")

results = cerebro.run()
strat = results[0]

sharpe = strat.analyzers.sharpe.get_analysis()
dd = strat.analyzers.drawdown.get_analysis()
trades = strat.analyzers.trades.get_analysis()

Order Types

Backtrader supports complex order types critical for realistic crypto backtesting.

Market Order

self.buy()  # market buy
self.sell()  # market sell
self.close()  # close current position

Limit Order

self.buy(exectype=bt.Order.Limit, price=95.0)
self.sell(exectype=bt.Order.Limit, price=105.0)

Stop Order

Triggers a market order when price reaches the stop level:

self.sell(exectype=bt.Order.Stop, price=90.0)  # stop loss

Stop-Limit Order

Triggers a limit order when price reaches the stop level:

self.buy(exectype=bt.Order.StopLimit, price=100.0, plimit=101.0)

Bracket Order

Entry + stop loss + take profit as an atomic unit. If the stop fills, the take profit is canceled (and vice versa).

self.buy_bracket(
    price=100.0,           # entry limit
    stopprice=95.0,        # stop loss
    limitprice=110.0,      # take profit
    exectype=bt.Order.Limit,
    stopexec=bt.Order.Stop,
    limitexec=bt.Order.Limit,
)

See references/strategy_patterns.md for bracket order patterns with ATR-based stops.

Position Sizing (Sizers)

Sizers determine how many units to buy/sell per order.

# Fixed size
cerebro.addsizer(bt.sizers.FixedSize, stake=100)

# Percent of portfolio
cerebro.addsizer(bt.sizers.PercentSizer, percents=95)

# All available cash
cerebro.addsizer(bt.sizers.AllInSizer, percents=95)

Custom sizer:

class RiskSizer(bt.Sizer):
    params = (("risk_pct", 0.02),)

    def _getsizing(self, comminfo, cash, data, isbuy):
        risk_amount = cash * self.p.risk_pct
        atr = self.strategy.atr[0]
        if atr <= 0:
            return 0
        size = risk_amount / atr
        return int(size)

Crypto Considerations

24/7 Markets

Crypto trades around the clock. When using daily bars, there are no weekends to skip. Set the session times or use sessionstart/sessionend if analyzing specific windows.

High Fees

DEX swaps on Solana typically cost 0.25-0.30% per trade. Set commission accordingly:

cerebro.broker.setcommission(commission=0.003)  # 0.3% round trip per side

Fractional Sizing

Crypto allows fractional units. Backtrader supports this natively -- no special config needed.

Slippage

For realistic simulation, enable cheat-on-open and add slippage:

cerebro.broker.set_coo(True)
cerebro.broker.set_slippage_perc(0.001)  # 0.1% slippage

Volatile Data

Crypto OHLCV data often has extreme wicks. Use ATR-based stops rather than fixed percentage stops to adapt to volatility.

Multi-Timeframe

Backtrader can resample data to multiple timeframes within a single strategy:

data_1h = bt.feeds.PandasData(dataname=df_1h)
cerebro.adddata(data_1h)

# Resample 1h to daily
cerebro.resampledata(data_1h, timeframe=bt.TimeFrame.Days, compression=1)

Access in strategy:

def __init__(self):
    self.ema_1h = bt.ind.EMA(self.datas[0], period=20)    # hourly
    self.ema_daily = bt.ind.EMA(self.datas[1], period=20)  # daily

Custom Indicators

class SpreadIndicator(bt.Indicator):
    lines = ("spread", "zscore",)
    params = (("period", 20),)

    def __init__(self):
        mean = bt.ind.SMA(self.data, period=self.p.period)
        std = bt.ind.StdDev(self.data, period=self.p.period)
        self.lines.spread = self.data - mean
        self.lines.zscore = self.lines.spread / std

Plotting

Backtrader includes matplotlib-based plotting:

cerebro.plot(style="candlestick", volume=True)

For headless environments, save to file:

import matplotlib
matplotlib.use("Agg")
figs = cerebro.plot(style="candlestick")
figs[0][0].savefig("backtest_result.png", dpi=150)

Integration with Other Skills

pandas-ta: Compute indicators externally, add as data feed columns. See references/api_guide.md for adding extra lines.
trading-visualization: Export trade log from notify_trade and plot with the visualization skill.
position-sizing: Use the position-sizing skill for Kelly or volatility-targeting sizers.
risk-management: Apply portfolio-level guardrails from the risk-management skill as strategy filters.
slippage-modeling: Use slippage estimates from the slippage-modeling skill to configure set_slippage_perc.

Files

References

references/api_guide.md — Cerebro, Strategy, Broker, Analyzer, Data Feed API reference
references/strategy_patterns.md — Reusable strategy patterns: crossover, mean reversion, multi-timeframe, custom indicators

Scripts

scripts/backtest_strategy.py — Complete EMA crossover backtest with analyzers and synthetic data
scripts/bracket_orders.py — Bracket order demonstration with RSI entry and ATR-based stops

Quick Start

uv pip install backtrader pandas numpy matplotlib
python scripts/backtest_strategy.py --demo
python scripts/bracket_orders.py --demo

FAQ

What does the backtrader skill do?

Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators

How do I install backtrader?

Run npx skills add https://github.com/agiprolabs/claude-trading-skills --skill backtrader in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/backtrader/ (personal) or .claude/skills/backtrader/ (project).

Which AI agents can use backtrader?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is backtrader safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains backtrader?

It is published by agiprolabs in agiprolabs/claude-trading-skills. It has 504 recorded installs.

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backtrader

Install

Install

Describe the task

Review the result

SKILL.md

Backtrader

Event-Driven vs Vectorized

Core Concepts

1. Cerebro (the engine)

2. Strategy (your logic)

3. Data Feed

4. Broker

5. Analyzers

Order Types

Market Order

Limit Order

Stop Order

Stop-Limit Order

Bracket Order

Position Sizing (Sizers)

Crypto Considerations

24/7 Markets

High Fees

Fractional Sizing

Slippage

Volatile Data

Multi-Timeframe

Custom Indicators

Plotting

Integration with Other Skills

Files

References

Scripts

Quick Start

FAQ

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