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Strip backtesting, rebalance indicator weights toward leading signals

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Remove backtest.py, backtest_gpu.py, metrics.py, local CSV data,
Dukascopy loader, and all backtest CLI modes from main.py. Rebalance
engine weights: leading indicators (divergence, RSI, stochastic, BB)
now carry 66% of signal weight vs 34% for lagging (EMA, MACD). Add
ws_twelvedata.py for live tick streaming. Drop pyarrow and tqdm deps.
This commit is contained in:
Harwinder
2026-05-10 23:40:15 -04:00
parent 2e09237671
commit 7ccd8c4b04
16 changed files with 82 additions and 234029 deletions
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engine.py
+12 -14
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@@ -171,7 +171,7 @@ class TechnicalEngine:
result.indicators["ema_trend"] = IndicatorSignal(
name="EMA Trend", value=normalized,
signal=signal, confidence=conf,
description=desc, weight=1.8,
description=desc, weight=1.0,
)
if t is not None:
result.key_levels["ema_trend"] = float(t)
@@ -222,7 +222,7 @@ class TechnicalEngine:
result.indicators["rsi"] = IndicatorSignal(
name="RSI", value=rsi_val, signal=signal,
confidence=conf, description=desc, weight=0.0,
confidence=conf, description=desc, weight=1.8,
)
# Attach RSI series for divergence detection (dynamic attribute, filtered by _prefix)
@@ -291,7 +291,7 @@ class TechnicalEngine:
result.indicators["macd"] = IndicatorSignal(
name="MACD", value=hist_val, signal=signal,
confidence=conf, description=desc, weight=3.3,
confidence=conf, description=desc, weight=1.2,
)
# Attach histogram for divergence detection
@@ -358,7 +358,7 @@ class TechnicalEngine:
result.indicators["bollinger"] = IndicatorSignal(
name="Bollinger Bands", value=bb_position, signal=signal,
confidence=conf, description=desc, weight=0.0,
confidence=conf, description=desc, weight=1.5,
)
result.key_levels["bb_upper"] = upper_val
@@ -423,7 +423,7 @@ class TechnicalEngine:
result.indicators["stochastic"] = IndicatorSignal(
name="Stochastic", value=k_val, signal=signal,
confidence=conf, description=desc, weight=2.4,
confidence=conf, description=desc, weight=2.5,
)
def _compute_adx(self, df: pd.DataFrame, result: TechnicalAnalysis):
@@ -609,7 +609,7 @@ class TechnicalEngine:
name="Ichimoku", value=float(score), signal=signal,
confidence=conf,
description=f"Ichimoku [{score}/4]: {' | '.join(signals)}",
weight=0.55,
weight=0.3,
)
result.key_levels["ichimoku_cloud_top"] = cloud_top
@@ -672,7 +672,7 @@ class TechnicalEngine:
name="Divergence", value=len(divergences),
signal=signal, confidence=70,
description=" | ".join(divergences),
weight=1.5, # divergences are strong reversal signals
weight=3.5,
)
# Note: divergences are NOT added to pattern_signals to avoid
# duplicate reason collection (the indicator entry covers them).
@@ -742,7 +742,7 @@ class TechnicalEngine:
name="Candlestick", value=float(bullish - bearish),
signal=signal, confidence=50,
description=" | ".join(patterns),
weight=0.7,
weight=1.2,
)
result.pattern_signals.extend(patterns)
@@ -784,7 +784,7 @@ class TechnicalEngine:
result.indicators["pivot"] = IndicatorSignal(
name="Pivot Points", value=pivot,
signal=SignalStrength.BUY if latest > pivot else SignalStrength.SELL,
confidence=45, description=desc, weight=0.6,
confidence=45, description=desc, weight=0.8,
)
def _compute_volume_analysis(self, df: pd.DataFrame, result: TechnicalAnalysis):
@@ -854,12 +854,10 @@ class TechnicalEngine:
total_weight = 0.0
total_confidence = 0.0
# Regime-gate ichimoku: trend hero (-23% to disable in 2024 trend) but
# chop villain (+18% to disable in 2023 chop & 2026 recent).
regime_weights = {
"trending_up": {"ema_trend": 1.5, "adx": 1.3, "macd": 1.2, "ichimoku": 1.3, "rsi": 0.7, "bollinger": 0.6},
"trending_down": {"ema_trend": 1.5, "adx": 1.3, "macd": 1.2, "ichimoku": 1.3, "rsi": 0.7, "bollinger": 0.6},
"ranging": {"rsi": 1.4, "stochastic": 1.3, "bollinger": 1.4, "ema_trend": 0.6, "adx": 0.5, "ichimoku": 0.2},
"trending_up": {"divergence": 1.5, "rsi": 1.2, "stochastic": 1.1, "candlestick": 1.2, "ema_trend": 1.0, "macd": 1.0, "adx": 1.0, "ichimoku": 0.8, "bollinger": 0.7, "pivot": 0.8},
"trending_down": {"divergence": 1.5, "rsi": 1.2, "stochastic": 1.1, "candlestick": 1.2, "ema_trend": 1.0, "macd": 1.0, "adx": 1.0, "ichimoku": 0.8, "bollinger": 0.7, "pivot": 0.8},
"ranging": {"divergence": 1.3, "rsi": 1.4, "stochastic": 1.4, "bollinger": 1.5, "candlestick": 1.3, "pivot": 1.3, "ema_trend": 0.5, "macd": 0.6, "adx": 0.4, "ichimoku": 0.2},
}
active_regime_weights = regime_weights.get(result.market_regime, {})