250 lines
8.8 KiB
Python
250 lines
8.8 KiB
Python
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#!/usr/bin/env python3
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"""Train XGBoost lock model V2 — pre-trigger features only (no data leakage).
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Targets ask<0.85 trades where the real edge is.
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Features computed ONLY from snapshots BEFORE trigger moment.
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Usage:
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cd ~/btc_15m_collab/rewrite
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source .venv/bin/activate
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python3 tools/xgb_lock_train_v2.py
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"""
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import json
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import numpy as np
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import pandas as pd
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import xgboost as xgb
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from sklearn.metrics import roc_auc_score
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from collections import defaultdict
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PAPER_FILE = "data/chainlink_predictor/paper_trades.jsonl"
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SNAPSHOT_FILE = "data/chainlink_predictor/snapshots.jsonl"
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MODEL_PATH = "models/lock_xgb.json"
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FEATURES_PATH = "models/lock_xgb_features.json"
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# New feature set — all computable from pre-trigger snapshots
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FEATURE_NAMES = [
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"ask", # our side's CLOB ask at trigger
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"abs_pm_a", # |pm_a| at trigger
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"left", # seconds remaining when triggered
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"rv_5m", # 5-min realized volatility
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"rv_15m", # 15-min realized volatility
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"sim_a_vol", # exchange price divergence
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"ask_margin", # our_ask - opp_ask (CLOB directional confidence)
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"pm_a_accel", # |pm_a| growth in last 30s before trigger
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"ask_vel", # ask price change rate before trigger
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"vol_ratio", # our volume / total volume
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"total_vol", # total CLOB volume (USDC)
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"sign_changes", # pm_a sign flips before trigger (regime)
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"pm_a_std", # pm_a standard deviation (stability)
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"diff", # |sim - pm| at trigger (CB-PM divergence proxy)
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"trades_count", # CLOB trade count
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]
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def build_snapshot_index():
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"""Index snapshots by window_id."""
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print("Loading snapshots...")
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snaps_by_wid = defaultdict(list)
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with open(SNAPSHOT_FILE, "rb") as f:
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f.seek(0, 2)
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sz = f.tell()
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for offset in range(0, sz, 200_000_000):
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f.seek(offset)
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f.readline()
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chunk = f.read(200_000_000)
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for line in chunk.split(b"\n"):
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if not line:
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continue
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try:
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s = json.loads(line)
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wid = str(s.get("window_id", ""))
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if not wid:
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continue
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clob = s.get("clob", {})
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snaps_by_wid[wid].append({
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"left": s.get("left_sec", 0),
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"pm_a": s.get("pm_a", 0),
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"rv_5m": s.get("rv_5m", 0),
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"rv_15m": s.get("rv_15m", 0),
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"diff": s.get("diff", 0),
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"sim_a_vol": s.get("sim_a_vol", 0),
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"up_ask": clob.get("up_ask", 0),
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"down_ask": clob.get("down_ask", 0),
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"up_vol": clob.get("up_buy_usdc", 0),
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"down_vol": clob.get("down_buy_usdc", 0),
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"trades": clob.get("trades", 0),
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})
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except Exception:
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pass
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print(f" {len(snaps_by_wid)} windows indexed")
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return snaps_by_wid
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def compute_pretrigger_features(snaps, trigger_left, direction):
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"""Compute features using ONLY snapshots BEFORE trigger moment."""
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before = [s for s in snaps if s["left"] > trigger_left]
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if len(before) < 5:
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return None
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our_ask_key = "up_ask" if direction == "UP" else "down_ask"
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opp_ask_key = "down_ask" if direction == "UP" else "up_ask"
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at_trig = before[-1] # closest to trigger
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our_ask = at_trig[our_ask_key]
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opp_ask = at_trig[opp_ask_key]
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our_asks = [s[our_ask_key] for s in before if s[our_ask_key] > 0]
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if not our_asks:
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return None
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pma_vals = [abs(s["pm_a"]) for s in before]
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# pm_a acceleration (last 30s before trigger vs earlier)
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recent = [s for s in before if s["left"] <= trigger_left + 35]
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earlier = [s for s in before if s["left"] > trigger_left + 35]
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if recent and earlier:
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pm_a_accel = np.mean([abs(s["pm_a"]) for s in recent]) - np.mean([abs(s["pm_a"]) for s in earlier])
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ask_vel = (our_asks[-1] - our_asks[0]) / max(len(our_asks), 1) if len(our_asks) > 3 else 0
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else:
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pm_a_accel = 0
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ask_vel = 0
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# Volume
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our_vol = at_trig["up_vol"] if direction == "UP" else at_trig["down_vol"]
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opp_vol = at_trig["down_vol"] if direction == "UP" else at_trig["up_vol"]
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total_vol = our_vol + opp_vol
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vol_ratio = our_vol / (total_vol + 1)
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# Regime
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signs = np.sign([s["pm_a"] for s in before])
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sign_changes = int(np.sum(np.diff(signs) != 0))
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pm_a_std = float(np.std(pma_vals))
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return {
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"ask_margin": our_ask - opp_ask,
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"pm_a_accel": pm_a_accel,
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"ask_vel": ask_vel,
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"vol_ratio": vol_ratio,
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"total_vol": total_vol,
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"sign_changes": sign_changes,
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"pm_a_std": pm_a_std,
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"rv_5m": at_trig["rv_5m"],
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"rv_15m": at_trig.get("rv_15m", 0),
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"sim_a_vol": at_trig["sim_a_vol"],
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"diff": abs(at_trig["diff"]),
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"trades_count": at_trig["trades"],
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}
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def load_data(snaps_by_wid):
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"""Load lock paper trades and compute pre-trigger features."""
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rows = []
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with open(PAPER_FILE) as f:
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for line in f:
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try:
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d = json.loads(line)
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except Exception:
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continue
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if d.get("strategy") != "lock" or "won" not in d:
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continue
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if abs(d.get("pm_a", 0)) < 60:
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continue
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wid = str(d.get("window_id", ""))
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snaps = snaps_by_wid.get(wid, [])
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trigger_left = d.get("left", 140)
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direction = d.get("direction", "UP")
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feats = compute_pretrigger_features(snaps, trigger_left, direction)
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if feats is None:
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continue
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row = {
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"won": int(bool(d["won"])),
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"ask": d["ask"],
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"abs_pm_a": abs(d["pm_a"]),
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"left": trigger_left,
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}
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row.update(feats)
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rows.append(row)
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return pd.DataFrame(rows)
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def main():
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snaps = build_snapshot_index()
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df = load_data(snaps)
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print(f"\nLoaded {len(df)} lock trades (pm_a>=60), WR={df['won'].mean():.1%}")
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# Show ask distribution
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cheap = df[df["ask"] < 0.85]
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expensive = df[df["ask"] >= 0.85]
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print(f" ask<0.85: {len(cheap)} trades, WR={cheap['won'].mean():.1%}")
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print(f" ask>=0.85: {len(expensive)} trades, WR={expensive['won'].mean():.1%}")
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# Train on ALL trades (model learns ask is important feature)
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X = df[FEATURE_NAMES]
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y = df["won"]
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# Time-based split
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split = int(len(df) * 0.7)
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X_train, X_test = X.iloc[:split], X.iloc[split:]
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y_train, y_test = y.iloc[:split], y.iloc[split:]
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print(f"\nTrain: {len(X_train)} (WR={y_train.mean():.1%})")
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print(f"Test: {len(X_test)} (WR={y_test.mean():.1%})")
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model = xgb.XGBClassifier(
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n_estimators=200, max_depth=3, learning_rate=0.05,
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subsample=0.8, colsample_bytree=0.7,
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eval_metric="logloss", random_state=42,
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)
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model.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)
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proba = model.predict_proba(X_test)[:, 1]
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auc = roc_auc_score(y_test, proba)
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print(f"\nOut-of-sample AUC: {auc:.3f}")
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# Threshold analysis — focus on ask<0.85 trades in test set
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print(f"\nThreshold analysis (test set, ask<0.85 only):")
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cheap_mask = X_test["ask"] < 0.85
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cheap_proba = proba[cheap_mask]
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cheap_y = y_test[cheap_mask]
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cheap_X = X_test[cheap_mask]
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print(f" Total cheap trades in test: {cheap_mask.sum()}")
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for thresh in [0.50, 0.60, 0.70, 0.75, 0.80, 0.85, 0.90]:
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keep = cheap_proba >= thresh
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if keep.sum() == 0:
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continue
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wr = cheap_y[keep].mean()
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n = keep.sum()
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block_n = (~keep).sum()
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asks = cheap_X.loc[keep, "ask"]
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pnl = sum((1 - asks.iloc[i]) * 5 if cheap_y[keep].iloc[i] else -asks.iloc[i] * 5 for i in range(n))
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print(f" >={thresh:.2f}: KEEP {n:3d} WR={wr:.0%} PnL=${pnl:+.1f} | BLOCK {block_n:3d}")
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# Feature importance
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print(f"\nTop features:")
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importance = sorted(zip(FEATURE_NAMES, model.feature_importances_), key=lambda x: -x[1])
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for name, score in importance:
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print(f" {name:>15s} {score:.3f}")
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# Train final model on ALL data
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print(f"\nTraining final model on all {len(df)} samples...")
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model_final = xgb.XGBClassifier(
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n_estimators=200, max_depth=3, learning_rate=0.05,
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subsample=0.8, colsample_bytree=0.7,
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eval_metric="logloss", random_state=42,
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)
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model_final.fit(X, y, verbose=False)
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model_final.save_model(MODEL_PATH)
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print(f"Model saved to {MODEL_PATH}")
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with open(FEATURES_PATH, "w") as f:
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json.dump(FEATURE_NAMES, f)
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print(f"Feature names saved to {FEATURES_PATH}")
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if __name__ == "__main__":
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main()
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