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8035461 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | #!/usr/bin/env python3
"""Live demo — stream Binance WebSocket, run trained TCN, fire alerts.
Connects to Binance WebSocket, maintains a 200-tick feature window,
runs the trained TCN on each new tick, and prints alerts to console
with colored output.
Usage:
python scripts/live_demo.py --model models/stage3_tcn_trained.pt --symbol BTCUSDT
python scripts/live_demo.py --model models/stage3_tcn_trained.pt --symbol BTCUSDT --threshold 0.3
"""
import argparse
import asyncio
import json
import logging
import signal
import sys
import time
from collections import deque
from datetime import datetime
from pathlib import Path
import numpy as np
import torch
ML_DIR = Path(__file__).resolve().parent.parent / "ml"
sys.path.insert(0, str(ML_DIR))
PROJECT_ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(PROJECT_ROOT))
from flash_crash_watchdog.features import FEATURE_NAMES, FeatureExtractor
from flash_crash_watchdog.models.stage3_tcn import TCNDetector, TCNConfig
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger(__name__)
TCN_FEATURES = FEATURE_NAMES[:17]
WINDOW_SIZE = 200
# ANSI colors for console output
class Color:
RED = '\033[91m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
BLUE = '\033[94m'
CYAN = '\033[96m'
BOLD = '\033[1m'
END = '\033[0m'
def load_trained_tcn(model_path: str, device: str = "auto") -> TCNDetector:
if device == "auto":
device = "cuda" if torch.cuda.is_available() else "cpu"
data = torch.load(model_path, map_location=device, weights_only=False)
config = data["config"]
model = TCNDetector(config).to(device)
model.load_state_dict(data["model_state"])
model.eval()
return model, device
async def run_live_demo(model: TCNDetector, device: str, symbol: str,
threshold: float, alert_log: str) -> None:
"""Stream Binance WebSocket and run the trained TCN in real time."""
import websockets
from flash_crash_watchdog.lob import OrderBookSnapshot, PriceLevel
from flash_crash_watchdog.tick import Tick, Trade
extractor = FeatureExtractor()
feature_window = deque(maxlen=WINDOW_SIZE)
url = f"wss://stream.binance.com:9443/stream?streams={symbol.lower()}@depth20@100ms/{symbol.lower()}@trade"
ticks_processed = 0
alerts_fired = 0
start_time = time.time()
# Console header
print(f"\n{Color.BOLD}{Color.CYAN}{'='*60}")
print(f" FLASH CRASH EARLY WARNING — LIVE DEMO")
print(f" Symbol: {symbol}")
print(f" Model: TCN (trained on real crash data)")
print(f" Threshold: {threshold}")
print(f" Window: {WINDOW_SIZE} ticks")
print(f" Press Ctrl+C to stop")
print(f"{'='*60}{Color.END}\n")
log_file = open(alert_log, "a") if alert_log else None
def print_alert(score, price, features):
nonlocal alerts_fired
alerts_fired += 1
ts = datetime.now().strftime("%H:%M:%S.%f")[:-3]
obi = features.get("f2_obi_10", 0.0)
vol = features.get("f4_realized_vol_1s", 0.0)
vpin = features.get("f3_vpin", 0.0)
print(f"{Color.RED}{Color.BOLD}🚨 ALERT #{alerts_fired}{Color.END} "
f"{Color.YELLOW}{ts}{Color.END} "
f"score={score:.3f} price=${price:,.2f} "
f"OBI={obi:+.4f} VPIN={vpin:.4f} Vol={vol:.6f}")
if log_file:
log_file.write(json.dumps({
"timestamp": ts, "alert_num": alerts_fired,
"score": score, "price": price,
"obi_10": obi, "vpin": vpin, "realized_vol": vol,
}) + "\n")
log_file.flush()
# Reconnect loop
while True:
try:
async with websockets.connect(url, ping_interval=20, ping_timeout=60, max_size=8*1024*1024) as ws:
if ticks_processed == 0:
logger.info("Connected. Waiting for %d ticks to fill window...", WINDOW_SIZE)
else:
logger.info("Reconnected. Continuing (window preserved)...")
async for raw in ws:
msg = json.loads(raw)
if "data" not in msg:
continue
data = msg["data"]
stream = msg.get("stream", "")
if "depth" in stream:
bids = [PriceLevel(float(p), float(s)) for p, s in data.get("bids", [])[:20]]
asks = [PriceLevel(float(p), float(s)) for p, s in data.get("asks", [])[:20]]
ts_ms = int(time.time() * 1000)
tick = Tick(book=OrderBookSnapshot(timestamp_ms=ts_ms, bids=bids, asks=asks), symbol=symbol)
elif "trade" in stream:
price = float(data.get("p", 0))
size = float(data.get("q", 0))
is_buyer_maker = data.get("m", False)
side = "sell" if is_buyer_maker else "buy"
ts_ms = data.get("T", int(time.time() * 1000))
trade = Trade(timestamp_ms=ts_ms, price=price, size=size, side=side)
tick = Tick(
book=OrderBookSnapshot(timestamp_ms=ts_ms,
bids=[PriceLevel(price, size)],
asks=[PriceLevel(price, size)]),
trades=[trade], symbol=symbol)
else:
continue
features = extractor.extract(tick)
vec = np.array([features.get(f, 0.0) for f in TCN_FEATURES])
feature_window.append(vec)
ticks_processed += 1
if ticks_processed % 100 == 0 and ticks_processed <= WINDOW_SIZE:
pct = ticks_processed / WINDOW_SIZE * 100
sys.stdout.write(f"\r{Color.BLUE}Filling window: {pct:.0f}% ({ticks_processed}/{WINDOW_SIZE}){Color.END}")
sys.stdout.flush()
if ticks_processed == WINDOW_SIZE:
print(f"\n{Color.GREEN}Window filled. Running TCN detector...{Color.END}\n")
if len(feature_window) >= WINDOW_SIZE:
window_array = np.array(list(feature_window))
with torch.no_grad():
x = torch.FloatTensor(window_array).T.unsqueeze(0).to(device)
scores = model(x)
score = float(scores[0, -1].item())
if ticks_processed % 500 == 0:
price = tick.book.mid_price or 0.0
elapsed = time.time() - start_time
rate = ticks_processed / max(1, elapsed)
status = (f"{Color.BLUE}[{ticks_processed:>6} ticks | {rate:.0f}/s | {alerts_fired} alerts] "
f"score={score:.3f} price=${price:,.2f}{Color.END}")
sys.stdout.write(f"\r{status}")
sys.stdout.flush()
if score >= threshold:
price = tick.book.mid_price or 0.0
print()
print_alert(score, price, features)
except asyncio.CancelledError:
break
except Exception as e:
logger.error("WebSocket error: %s — reconnecting in 2s...", e)
await asyncio.sleep(2)
if log_file:
log_file.close()
print(f"\n\n{Color.CYAN}Demo ended. {ticks_processed} ticks processed, {alerts_fired} alerts fired.{Color.END}")
def main() -> int:
parser = argparse.ArgumentParser(description="Live flash-crash detection demo")
parser.add_argument("--model", required=True, help="Trained TCN model")
parser.add_argument("--symbol", default="BTCUSDT")
parser.add_argument("--threshold", type=float, default=0.3)
parser.add_argument("--log", default="", help="Alert log file (JSONL)")
args = parser.parse_args()
model, device = load_trained_tcn(args.model)
try:
asyncio.run(run_live_demo(model, device, args.symbol, args.threshold, args.log))
except KeyboardInterrupt:
print(f"\n{Color.YELLOW}Stopped by user.{Color.END}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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