agiprolabs
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backtrader
Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators
Registry indexed
Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators
Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators
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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.
| 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:
Use vectorbt when you need:
Backtrader has five core objects that interact through an event loop:
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()
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()
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
)
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)
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()
Backtrader supports complex order types critical for realistic crypto backtesting.
self.buy() # market buy
self.sell() # market sell
self.close() # close current position
self.buy(exectype=bt.Order.Limit, price=95.0)
self.sell(exectype=bt.Order.Limit, price=105.0)
Triggers a market order when price reaches the stop level:
self.sell(exectype=bt.Order.Stop, price=90.0) # stop loss
Triggers a limit order when price reaches the stop level:
self.buy(exectype=bt.Order.StopLimit, price=100.0, plimit=101.0)
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.
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 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.
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
Crypto allows fractional units. Backtrader supports this natively -- no special config needed.
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
Crypto OHLCV data often has extreme wicks. Use ATR-based stops rather than fixed percentage stops to adapt to volatility.
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
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
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)
references/api_guide.md for adding extra lines.notify_trade and plot with the visualization skill.position-sizing skill for Kelly or volatility-targeting sizers.risk-management skill as strategy filters.slippage-modeling skill to configure set_slippage_perc.references/api_guide.md — Cerebro, Strategy, Broker, Analyzer, Data Feed API referencereferences/strategy_patterns.md — Reusable strategy patterns: crossover, mean reversion, multi-timeframe, custom indicatorsscripts/backtest_strategy.py — Complete EMA crossover backtest with analyzers and synthetic datascripts/bracket_orders.py — Bracket order demonstration with RSI entry and ATR-based stopsuv pip install backtrader pandas numpy matplotlib
python scripts/backtest_strategy.py --demo
python scripts/bracket_orders.py --demo
name: backtrader description: Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators
---
name: backtrader
description: Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators
---
# 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()`.
```python
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.
```python
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:
```python
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:
```python
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.
```python
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.
```python
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
```python
self.buy() # market buy
self.sell() # market sell
self.close() # close current position
```
### Limit Order
```python
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:
```python
self.sell(exectype=bt.Order.Stop, price=90.0) # stop loss
```
### Stop-Limit Order
Triggers a limit order when price reaches the stop level:
```python
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).
```python
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.
```python
# 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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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
```python
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:
```python
cerebro.plot(style="candlestick", volume=True)
```
For headless environments, save to file:
```python
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
```bash
uv pip install backtrader pandas numpy matplotlib
python scripts/backtest_strategy.py --demo
python scripts/bracket_orders.py --demo
```
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Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
72/100
Strong
Trust
68/100
Sandbox only
Audit
80/100
Risky
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"recent_failure_rate": null,
"install_attempts": 0,
"install_success_rate": null,
"risk_blocked": 0,
"setup_required": 0,
"avg_output_quality": null,
"production_outcomes": 0,
"last_outcome_at": null,
"label": "No agent outcome data yet"
},
"auto_install": {
"allowed": false,
"sandbox_required": true,
"reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"best_for": [
"coding-agents",
"agent-skill"
],
"known_risks": [
"Financial research output is not financial advice; require human review before any live investment decision.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Quality score needs review",
"Permission surface needs review: shell or command execution, filesystem or document access",
"Dependency/runtime risk: command execution surface, external package install surface",
"Permission surface: shell or command execution, filesystem or document access"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 80,
"risk_level": "risky",
"risk_label": "Risky",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
"Financial research output is not financial advice; require human review before any live investment decision.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Quality score needs review",
"Permission surface needs review: shell or command execution, filesystem or document access"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 72,
"label": "Strong"
},
"supply": {
"track": "Coding and developer agents",
"scenario": "Coding agents",
"maintenance": "8d since push",
"risk": "Risky"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"high-compliance environments without internal security review",
"No OpenAgentSkill engagement data yet",
"Audit risk risky exceeds max_risk=medium",
"High-risk permission hints: Shell or command execution",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision"
],
"agent_contract": {
"task_input": "Use backtrader in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 76/100 Strong shortlist",
"Audit: 80/100 Risky",
"Safety: 52/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "agiprolabs-backtrader (backtrader)",
"install_command": "npx skills add agiprolabs/claude-trading-skills --skill backtrader",
"risk_summary": "Risky; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "agiprolabs-backtrader",
"task": "Use backtrader in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/agiprolabs-backtrader",
"api": "https://www.openagentskill.com/api/agent/skills/agiprolabs-backtrader",
"audit": "https://www.openagentskill.com/skills/agiprolabs-backtrader/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=agiprolabs-backtrader&task=Use%20backtrader%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20backtrader%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20backtrader%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/agiprolabs-backtrader/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/agiprolabs-backtrader"
}
}Listing source
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