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from dataclasses import dataclass
from io import StringIO
from typing import Any
import backtrader as bt
import pandas as pd
import yfinance as yf
from backtesting import Backtest, Strategy
@dataclass
class BacktestResult:
engine: str
metrics: dict[str, Any]
equity_curve: pd.DataFrame
trades: pd.DataFrame
input_rows: int
def _normalize_ohlcv_columns(df: pd.DataFrame) -> pd.DataFrame:
if isinstance(df.columns, pd.MultiIndex):
df.columns = df.columns.get_level_values(0)
rename_map = {}
for col in df.columns:
lower = str(col).lower()
if lower == "open":
rename_map[col] = "Open"
elif lower == "high":
rename_map[col] = "High"
elif lower == "low":
rename_map[col] = "Low"
elif lower == "close":
rename_map[col] = "Close"
elif lower == "volume":
rename_map[col] = "Volume"
normalized = df.rename(columns=rename_map).copy()
for required in ["Open", "High", "Low", "Close"]:
if required not in normalized.columns:
raise ValueError(f"Missing required OHLC column: {required}")
if "Volume" not in normalized.columns:
normalized["Volume"] = 0
if not isinstance(normalized.index, pd.DatetimeIndex):
normalized.index = pd.to_datetime(normalized.index, utc=True, errors="coerce")
if normalized.index.tz is not None:
normalized.index = normalized.index.tz_convert("UTC").tz_localize(None)
normalized = normalized.dropna(subset=["Open", "High", "Low", "Close"])
normalized = normalized.sort_index()
return normalized
def load_price_data_from_yfinance(
symbol: str,
start: str,
end: str,
interval: str = "1d",
) -> pd.DataFrame:
df = yf.download(symbol, start=start, end=end, interval=interval, auto_adjust=False)
if df is None or df.empty:
raise ValueError(f"No market data returned for symbol: {symbol}")
return _normalize_ohlcv_columns(df)
def load_price_data_from_csv_text(csv_text: str) -> pd.DataFrame:
df = pd.read_csv(StringIO(csv_text))
lowered = {str(c).lower(): c for c in df.columns}
if "date" in lowered:
date_col = lowered["date"]
df[date_col] = pd.to_datetime(df[date_col], utc=True, errors="coerce")
df = df.set_index(date_col)
elif "datetime" in lowered:
dt_col = lowered["datetime"]
df[dt_col] = pd.to_datetime(df[dt_col], utc=True, errors="coerce")
df = df.set_index(dt_col)
return _normalize_ohlcv_columns(df)
class SmaCrossBacktestingPy(Strategy):
fast_period = 10
slow_period = 30
def init(self) -> None:
close = pd.Series(self.data.Close)
self.fast = self.I(lambda x: pd.Series(x).rolling(self.fast_period).mean(), close)
self.slow = self.I(lambda x: pd.Series(x).rolling(self.slow_period).mean(), close)
def next(self) -> None:
if self.fast[-1] > self.slow[-1] and not self.position:
self.buy()
elif self.fast[-1] < self.slow[-1] and self.position:
self.position.close()
class SmaCrossBacktrader(bt.Strategy):
params = (("fast_period", 10), ("slow_period", 30))
def __init__(self) -> None:
self.fast = bt.indicators.SimpleMovingAverage(
self.data.close, period=self.params.fast_period
)
self.slow = bt.indicators.SimpleMovingAverage(
self.data.close, period=self.params.slow_period
)
self.crossover = bt.indicators.CrossOver(self.fast, self.slow)
self.equity_points: list[tuple[pd.Timestamp, float]] = []
def next(self) -> None:
if self.crossover > 0 and not self.position:
self.buy()
elif self.crossover < 0 and self.position:
self.close()
dt = self.data.datetime.datetime(0)
self.equity_points.append((pd.Timestamp(dt, tz="UTC"), self.broker.getvalue()))
def run_backtesting_py(
data: pd.DataFrame,
fast_period: int,
slow_period: int,
initial_cash: float,
commission: float,
) -> BacktestResult:
strategy_cls = type(
"ConfiguredSmaCrossBacktestingPy",
(SmaCrossBacktestingPy,),
{"fast_period": fast_period, "slow_period": slow_period},
)
bt_obj = Backtest(data, strategy_cls, cash=initial_cash, commission=commission)
stats = bt_obj.run()
equity_curve = stats.get("_equity_curve", pd.DataFrame())
trades = stats.get("_trades", pd.DataFrame())
metrics = {
"Return [%]": float(stats.get("Return [%]", 0.0)),
"Buy & Hold Return [%]": float(stats.get("Buy & Hold Return [%]", 0.0)),
"Sharpe Ratio": float(stats.get("Sharpe Ratio", 0.0) or 0.0),
"Max Drawdown [%]": float(stats.get("Max. Drawdown [%]", 0.0)),
"# Trades": int(stats.get("# Trades", 0)),
"Win Rate [%]": float(stats.get("Win Rate [%]", 0.0)),
}
return BacktestResult(
engine="backtesting.py",
metrics=metrics,
equity_curve=equity_curve,
trades=trades,
input_rows=len(data),
)
def run_backtrader(
data: pd.DataFrame,
fast_period: int,
slow_period: int,
initial_cash: float,
commission: float,
) -> BacktestResult:
cerebro = bt.Cerebro()
configured = type(
"ConfiguredSmaCrossBacktrader",
(SmaCrossBacktrader,),
{"params": (("fast_period", fast_period), ("slow_period", slow_period))},
)
cerebro.addstrategy(configured)
feed = bt.feeds.PandasData(dataname=data)
cerebro.adddata(feed)
cerebro.broker.setcash(initial_cash)
cerebro.broker.setcommission(commission=commission)
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe", timeframe=bt.TimeFrame.Days)
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
starting_value = cerebro.broker.getvalue()
run_result = cerebro.run()
ending_value = cerebro.broker.getvalue()
strategy = run_result[0]
dd = strategy.analyzers.drawdown.get_analysis()
sharpe = strategy.analyzers.sharpe.get_analysis()
ta = strategy.analyzers.trades.get_analysis()
total_closed = int(getattr(ta.total, "closed", 0) if hasattr(ta, "total") else 0)
won_total = int(getattr(ta.won, "total", 0) if hasattr(ta, "won") else 0)
win_rate = (won_total / total_closed * 100) if total_closed else 0.0
ret_pct = ((ending_value - starting_value) / starting_value * 100) if starting_value else 0.0
equity_curve = pd.DataFrame(strategy.equity_points, columns=["Time", "Equity"])
if not equity_curve.empty:
equity_curve = equity_curve.set_index("Time")
metrics = {
"Return [%]": float(ret_pct),
"Sharpe Ratio": float(sharpe.get("sharperatio", 0.0) or 0.0),
"Max Drawdown [%]": float(getattr(dd.max, "drawdown", 0.0) if hasattr(dd, "max") else 0.0),
"# Trades": total_closed,
"Win Rate [%]": float(win_rate),
"Final Equity": float(ending_value),
}
return BacktestResult(
engine="backtrader",
metrics=metrics,
equity_curve=equity_curve,
trades=pd.DataFrame(),
input_rows=len(data),
)
def run_backtest(
engine: str,
data: pd.DataFrame,
fast_period: int,
slow_period: int,
initial_cash: float,
commission: float,
) -> BacktestResult:
if fast_period >= slow_period:
raise ValueError("fast_period must be smaller than slow_period.")
if engine == "backtesting.py":
return run_backtesting_py(data, fast_period, slow_period, initial_cash, commission)
if engine == "backtrader":
return run_backtrader(data, fast_period, slow_period, initial_cash, commission)
raise ValueError(f"Unsupported engine: {engine}")
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