Baseline evaluation logics added
This commit is contained in:
Quanliang Liu
parent
b512027574
commit
c29ca882af
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@ -50,6 +50,7 @@ class TradingAgentsBacktester:
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print(f"Decision: {decision}")
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signal = self._parse_decision(decision)
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decisions.append({"date": date_str, "decision": decision, "signal": signal, "price": price})
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except Exception as e:
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print(f"Error: {e}")
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signal = 0
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@ -1,15 +1,10 @@
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"""
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Baseline trading strategies for comparison.
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Implements: Buy&Hold, MACD, KDJ+RSI, ZMR, SMA
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"""
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import pandas as pd
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import numpy as np
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from abc import ABC, abstractmethod
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class BaseStrategy(ABC):
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"""Base class for trading strategies."""
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"""Base class for trading strategies (long-only, action-based)."""
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def __init__(self, initial_capital=100000):
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self.initial_capital = float(initial_capital)
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@ -26,7 +21,13 @@ class BaseStrategy(ABC):
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@abstractmethod
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def generate_signals(self, data: pd.DataFrame) -> pd.Series:
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"""Generate *target* position by date (1 long, -1 short, 0 flat)."""
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"""
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Generate *actions* by date:
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1 = BUY (open / go long, or stay long)
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0 = HOLD (no change)
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-1 = SELL (exit to flat)
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Shorting is NOT allowed.
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"""
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pass
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def _prep_ohlcv(self, data: pd.DataFrame) -> pd.DataFrame:
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@ -36,25 +37,44 @@ class BaseStrategy(ABC):
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raise ValueError(f"Data missing column '{col}'")
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return data.copy()
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@staticmethod
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def _actions_to_position(actions: pd.Series) -> pd.Series:
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"""Convert action series to a long-only position series in {0,1}."""
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a = actions.astype(float).fillna(0.0).clip(-1, 1).values
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pos = np.zeros_like(a, dtype=float)
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for i in range(len(a)):
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if i == 0:
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pos[i] = 1.0 if a[i] > 0 else 0.0
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else:
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if a[i] > 0: # buy → long
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pos[i] = 1.0
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elif a[i] < 0: # sell → flat
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pos[i] = 0.0
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else: # hold → keep previous
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pos[i] = pos[i-1]
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return pd.Series(pos, index=actions.index, name="position")
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def backtest(self, data: pd.DataFrame) -> pd.DataFrame:
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df = self._prep_ohlcv(data)
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signals = self.generate_signals(df).astype(float)
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signals = signals.clip(lower=-1, upper=1).reindex(df.index).fillna(0)
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# ONE place for hold semantics (Option A: 0 = no new signal)
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position = signals.replace(0, np.nan).ffill().fillna(0)
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# 1) get actions (1, 0, -1)
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actions = self.generate_signals(df).reindex(df.index).fillna(0).clip(-1, 1).astype(float)
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# 2) map actions → long-only position {0,1}
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position = self._actions_to_position(actions)
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# 3) compute returns (note: sell today → flat tomorrow → 0 return tomorrow)
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close = self._close_series(df)
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market_ret = close.pct_change().fillna(0.0)
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exposure = position.shift(1).fillna(0.0)
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exposure = position.shift(1).fillna(0.0) # use yesterday's position
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strat_ret = (exposure * market_ret).astype(float)
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cumret = (1.0 + strat_ret).cumprod()
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portval = self.initial_capital * cumret
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portfolio = pd.DataFrame(index=df.index)
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portfolio["signal"] = signals
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portfolio["position"] = position
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portfolio["action"] = actions # 1 buy / 0 hold / -1 sell
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portfolio["position"] = position # 1 long / 0 flat
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portfolio["close"] = close
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if "Volume" in df.columns:
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vol = df["Volume"]
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@ -66,113 +86,82 @@ class BaseStrategy(ABC):
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portfolio["strategy_return"] = strat_ret
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portfolio["cumulative_return"] = cumret
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portfolio["portfolio_value"] = portval
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portfolio["trade"] = portfolio["position"].diff().fillna(0.0)
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portfolio["trade_delta"] = portfolio["position"].diff().fillna(0.0) # +1 buy, -1 sell
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return portfolio
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class BuyAndHoldStrategy(BaseStrategy):
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"""Buy at start and hold long the whole period (no short)."""
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"""Buy on day 1 and hold long (no shorting)."""
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def generate_signals(self, data: pd.DataFrame) -> pd.Series:
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s = pd.Series(1.0, index=data.index)
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return s
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a = pd.Series(0.0, index=data.index)
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if len(a) > 0:
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a.iloc[0] = 1.0 # buy once at start
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return a
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class MACDStrategy(BaseStrategy):
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"""
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MACD Strategy.
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Long when MACD > signal, Short when MACD < signal.
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"""
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"""MACD(12,26,9) Contrarian, long-only：MACD>signal → SELL(退出)，MACD<signal → BUY(做多)."""
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def generate_signals(self, data: pd.DataFrame) -> pd.Series:
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def generate_signals(self, data):
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df = data.copy()
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if "macd" not in df.columns or "macds" not in df.columns:
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df = self._calculate_macd(df)
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macd_diff = (df["macd"] - df["macds"]).fillna(0.0)
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sig = pd.Series(0.0, index=df.index)
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sig[macd_diff > 0] = 1.0
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sig[macd_diff < 0] = -1.0
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return sig
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ema_fast = df["Close"].ewm(span=12, adjust=False).mean()
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ema_slow = df["Close"].ewm(span=26, adjust=False).mean()
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macd = ema_fast - ema_slow
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signal = macd.ewm(span=9, adjust=False).mean()
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diff = macd - signal
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def _calculate_macd(self, data: pd.DataFrame, fast=12, slow=26, signal=9):
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exp1 = data["Close"].ewm(span=fast, adjust=False).mean()
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exp2 = data["Close"].ewm(span=slow, adjust=False).mean()
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macd = exp1 - exp2
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macds = macd.ewm(span=signal, adjust=False).mean()
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data["macd"] = macd
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data["macds"] = macds
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data["macdh"] = macd - macds
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return data
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a = pd.Series(0.0, index=df.index)
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a[diff > 0] = -1.0 # 卖出/退出（之前是做空）
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a[diff < 0] = 1.0 # 买入/做多
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return a
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class KDJRSIStrategy(BaseStrategy):
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"""
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KDJ & RSI Strategy (classic oversold/overbought gating).
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Long when RSI<30 & K<20; Short when RSI>70 & K>80.
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"""
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"""KDJ + RSI 逆势逻辑（长多-only）：超买 → 卖出；超卖 → 买入"""
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def generate_signals(self, data: pd.DataFrame) -> pd.Series:
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def generate_signals(self, data):
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df = data.copy()
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if "rsi" not in df.columns:
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df = self._calculate_rsi(df)
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if "kdj_k" not in df.columns:
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df = self._calculate_kdj(df)
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sig = pd.Series(0.0, index=df.index)
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sig[(df["rsi"] < 30) & (df["kdj_k"] < 20)] = 1.0
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sig[(df["rsi"] > 70) & (df["kdj_k"] > 80)] = -1.0
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return sig
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# === RSI ===
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delta = df["Close"].diff()
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up, down = delta.clip(lower=0), -delta.clip(upper=0)
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rs = up.ewm(span=14, adjust=False).mean() / down.ewm(span=14, adjust=False).mean().replace(0, np.nan)
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df["rsi"] = 100 - 100 / (1 + rs)
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def _calculate_rsi(self, data: pd.DataFrame, period=14):
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# Wilder's smoothing approximation via EMA improves stability
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delta = data["Close"].diff()
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up = delta.clip(lower=0)
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down = -delta.clip(upper=0)
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roll_up = up.ewm(alpha=1/period, adjust=False).mean()
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roll_down = down.ewm(alpha=1/period, adjust=False).mean()
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rs = roll_up / roll_down.replace(0, np.nan)
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data["rsi"] = 100 - (100 / (1 + rs))
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return data
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# === KDJ ===
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low = df["Low"].rolling(9).min()
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high = df["High"].rolling(9).max()
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denom = (high - low).replace(0, np.nan)
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rsv = 100 * (df["Close"] - low) / denom
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k = rsv.ewm(com=2, adjust=False).mean()
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df["kdj_k"] = k
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def _calculate_kdj(self, data: pd.DataFrame, period=9):
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low_min = data["Low"].rolling(window=period, min_periods=period).min()
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high_max = data["High"].rolling(window=period, min_periods=period).max()
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den = (high_max - low_min).replace(0, np.nan)
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rsv = 100 * (data["Close"] - low_min) / den
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k = rsv.ewm(com=2, adjust=False, min_periods=1).mean()
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d = k.ewm(com=2, adjust=False, min_periods=1).mean()
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j = 3 * k - 2 * d
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data["kdj_k"], data["kdj_d"], data["kdj_j"] = k, d, j
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return data
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# === Actions ===
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a = pd.Series(0.0, index=df.index)
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# 收紧阈值：RSI>75,K>85 → 卖出；RSI<25,K<15 → 买入
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a[(df["rsi"] > 75) & (df["kdj_k"] > 85)] = -1.0
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a[(df["rsi"] < 25) & (df["kdj_k"] < 15)] = 1.0
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return a
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class ZMRStrategy(BaseStrategy):
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"""
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Zero-mean reversion on z-score of Close vs rolling mean.
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"""
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def __init__(self, initial_capital=100000, lookback=20, threshold=1.0):
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super().__init__(initial_capital)
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self.lookback = int(lookback)
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self.threshold = float(threshold)
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def generate_signals(self, data: pd.DataFrame) -> pd.Series:
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def generate_signals(self, data):
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close = self._close_series(data)
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rm = close.rolling(window=self.lookback, min_periods=self.lookback).mean()
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rs = close.rolling(window=self.lookback, min_periods=self.lookback).std()
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z = (close - rm) / rs.replace(0, pd.NA)
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sig = pd.Series(0.0, index=data.index)
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sig[z < -self.threshold] = 1.0
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sig[z > self.threshold] = -1.0
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return sig
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mean = close.rolling(50).mean()
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std = close.rolling(50).std()
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z = (close - mean) / std.replace(0, np.nan)
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a = pd.Series(0.0, index=data.index)
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a[z > 1.3] = -1.0 # 高估 → 卖出/退出
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a[z < -1.3] = 1.0 # 低估 → 买入/做多
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return a
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class SMAStrategy(BaseStrategy):
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"""
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SMA crossover (50/200 by default).
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"""
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def __init__(self, initial_capital=100000, short_window=50, long_window=200):
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def __init__(self, initial_capital=100000, short_window=5, long_window=20):
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super().__init__(initial_capital)
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self.short_window = int(short_window)
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self.long_window = int(long_window)
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@ -181,18 +170,18 @@ class SMAStrategy(BaseStrategy):
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close = self._close_series(data)
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short = close.rolling(window=self.short_window, min_periods=self.short_window).mean()
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long_ = close.rolling(window=self.long_window, min_periods=self.long_window).mean()
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sig = pd.Series(0.0, index=data.index)
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sig[short > long_] = 1.0
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sig[short < long_] = -1.0
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return sig
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a = pd.Series(0.0, index=data.index)
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a[short > long_] = 1.0
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a[short < long_] = -1.0
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return a
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def get_all_baseline_strategies(initial_capital=100000):
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"""Get all baseline strategies for comparison."""
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"""Get all baseline strategies for comparison (long-only, action-based)."""
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return {
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"BuyAndHold": BuyAndHoldStrategy(initial_capital),
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"MACD": MACDStrategy(initial_capital),
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"KDJ&RSI": KDJRSIStrategy(initial_capital),
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"ZMR": ZMRStrategy(initial_capital),
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"SMA": SMAStrategy(initial_capital),
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}
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}
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@ -83,8 +83,8 @@ def run_evaluation(
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try:
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cfg = (config or DEFAULT_CONFIG).copy()
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# Fast eval defaults (you can override from CLI)
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cfg["deep_think_llm"] = cfg.get("deep_think_llm", "gpt-4o-mini")
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cfg["quick_think_llm"] = cfg.get("quick_think_llm", "gpt-4o-mini")
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cfg["deep_think_llm"] = cfg.get("deep_think_llm", "gpt-5-nano")
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cfg["quick_think_llm"] = cfg.get("quick_think_llm", "gpt-5-nano")
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cfg["max_debate_rounds"] = cfg.get("max_debate_rounds", 1)
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cfg["max_risk_discuss_rounds"] = cfg.get("max_risk_discuss_rounds", 1)
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# Deterministic-ish decoding for reproducibility
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@ -154,7 +154,7 @@ def run_evaluation(
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def main():
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parser = argparse.ArgumentParser(description="Run TradingAgents evaluation with baseline comparisons")
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parser.add_argument("ticker", type=str, help="Stock ticker symbol (e.g., AAPL)")
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parser.add_argument("--ticker", type=str, help="Stock ticker symbol (e.g., AAPL)")
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parser.add_argument("--start-date", type=str, required=True, help="Start date (YYYY-MM-DD)")
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parser.add_argument("--end-date", type=str, required=True, help="End date (YYYY-MM-DD)")
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parser.add_argument("--capital", type=float, default=100000, help="Initial capital (default: 100000)")
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@ -169,8 +169,8 @@ def main():
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if is_debugging():
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config = DEFAULT_CONFIG.copy()
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config.update({
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"deep_think_llm": "gpt-4o-mini",
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"quick_think_llm": "gpt-4o-mini",
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"deep_think_llm": "gpt-5-nano",
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"quick_think_llm": "gpt-5-nano",
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"max_debate_rounds": 1,
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"max_risk_discuss_rounds": 1,
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"llm_params": {"temperature": 0, "top_p": 1.0, "seed": 42},
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@ -178,9 +178,9 @@ def main():
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run_evaluation(
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ticker="AAPL",
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start_date="2024-01-01",
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end_date="2024-03-30",
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end_date="2024-01-04",
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initial_capital=1000,
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include_tradingagents=False,
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include_tradingagents=True,
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output_dir="./evaluation/results",
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config=config
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)
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@ -206,4 +206,4 @@ def main():
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)
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if __name__ == "__main__":
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main()
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main()
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