backtrader Checked

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.