ramseshk
fb231eef7c
Root cause: 6 BTC strategies shared size=0.0002. Fill attribution by size-matching always credited fills to first strategy in dict (Order Book Imbalance), leaving other 5 with zero attributed fills. Fix: OBI: 0.000200 (unchanged) Iceberg: 0.000210 (+5%) Funding: 0.000220 (+10%) A-S: 0.000230 (+15%) Momentum:0.000240 (+20%) MeanRev: 0.000250 (+25%) Matching tolerance tightened 1e-5 → 1e-6 for unambiguous attribution. Also fixed MAINNET_INFO → TESTNET_API undefined variable.
453 lines
24 KiB
Python
453 lines
24 KiB
Python
"""
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Profitable HFT node — tight POST-ONLY quotes at best bid/ask.
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Uses real orderbook to place maker orders AT the best bid/ask level,
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not at mid ± random spread. Refreshes quotes every cycle to stay
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at queue front. Avellaneda-Stoikov places dual-sided quotes simultaneously.
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7 strategies x 100 USDC | Maker: 0.02% | Hyperliquid Testnet.
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"""
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import os, sys, asyncio, json, time, logging, random, math
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from pathlib import Path
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from datetime import datetime
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from collections import deque
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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import requests
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from nautilus_trader.core.nautilus_pyo3 import (
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HyperliquidHttpClient, HyperliquidEnvironment,
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UUID4, ClientOrderId, OrderSide, OrderType, TimeInForce,
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Quantity, Price,
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)
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(name)s] %(message)s", datefmt="%H:%M:%S")
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log = logging.getLogger("ftdt-quant")
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METRICS_FILE = "/tmp/ftdt-metrics.json"
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TESTNET_API = "https://api.hyperliquid-testnet.xyz/info"
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TOTAL_EQUITY = 898.0
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RESERVE = 398.0
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MAKER_FEE = 0.0002
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STRATEGIES = {
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"Order Book Imbalance": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.000250,"fee_paid":0.0,"signals":[],"type":"reversal","description":"L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate."},
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"Iceberg Detection": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"momentum","description":"Detects whale TWAP accumulation — follows smart money flow."},
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"Funding Rate Arb": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"carry","description":"Delta-neutral carry — holds spot, shorts perp, collects funding."},
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"Pairs Trading": {"allocation":100.0,"instrument":"ETH-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.006,"fee_paid":0.0,"signals":[],"type":"stat_arb","description":"BTC/ETH ratio Z-score — trades when spread exceeds 1.5σ."},
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"Avellaneda-Stoikov": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"market_making","description":"Dual-sided quoting at best bid/ask — captures spread via stochastic control. Places both sides simultaneously."},
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"Momentum Breakout": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"momentum","description":"Bollinger Band (2σ) breakout — enters with volume confirmation."},
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"Mean Reversion": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"reversal","description":"VWAP deviation — buys below VWAP, sells above. Oscillates around fair value."},
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"Kalman Pairs": {"allocation":100.0,"instrument":"ETH-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.006,"fee_paid":0.0,"signals":[],"type":"stat_arb","description":"Kalman-filter adaptive hedge ratio — tracks evolving BTC/ETH beta with every tick."}
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}
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trades_log: list[dict] = []
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equity_history: list[dict] = []
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strategy_equity: dict[str, list] = {}
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seen_fills: set[int] = set()
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btc_prices: deque = deque(maxlen=60)
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eth_prices: deque = deque(maxlen=60)
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active_cloids: dict = {} # Track active order IDs per strategy
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active_cloids_times: dict = {} # Tick when order was placed
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active_cloids_px: dict = {} # Entry price for take-profit
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# ═══════════════════════ Helpers ═══════════════════════
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def load_key():
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key = os.getenv("HYPERLIQUID_TESTNET_PK")
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if key: return key
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env_file = Path(__file__).resolve().parent.parent / ".env"
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if env_file.exists():
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for line in env_file.read_text().splitlines():
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if line.startswith("HYPERLIQUID_TESTNET_PK="):
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return line.split("=", 1)[1].strip()
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return None
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def get_fills(addr):
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r = requests.post(TESTNET_API, json={"type":"userFills","user":addr}, timeout=10)
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return r.json() if r.status_code==200 else []
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def get_mark_prices():
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try:
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r = requests.post(TESTNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
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data = r.json()
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if not data or data[0] is None or "universe" not in data[0]:
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return {}
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prices = {}
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for i,u in enumerate(data[0]["universe"]):
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if u["name"] in ("BTC","ETH"):
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prices[u["name"]] = float(data[1][i]["markPx"])
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return prices
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except Exception:
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return {}
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def get_orderbook(coin):
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"""Get best bid, best ask, and mid from L2 orderbook."""
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try:
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r = requests.post(TESTNET_API, json={"type":"l2Book","coin":coin}, timeout=10)
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data = r.json()
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best_bid = float(data["levels"][0][0]["px"]) if data["levels"][0] else 0
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best_ask = float(data["levels"][1][0]["px"]) if data["levels"][1] else 0
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return best_bid, best_ask, (best_bid+best_ask)/2 if best_bid and best_ask else 0
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except: return 0,0,0
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def write_metrics(addr):
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total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
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total_pnl_pct = (total_pnl/TOTAL_EQUITY)*100 if TOTAL_EQUITY>0 else 0
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for s in STRATEGIES.values():
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if s["trades_today"]>0: s["win_rate"] = s["wins"]/s["trades_today"]
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data = {
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"timestamp":time.time(),"wallet":addr,
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"total_equity":TOTAL_EQUITY+total_pnl,"base_equity":TOTAL_EQUITY,
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"total_pnl":total_pnl,"total_pnl_pct":total_pnl_pct,
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"reserve":RESERVE,"equity_history":equity_history[-600:],
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"strategies":STRATEGIES,"trades":trades_log[-200:],"status":"running","testnet_up":True,
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"strategy_equity":{k: v[-600:] for k,v in strategy_equity.items()},
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"open_positions":[],"open_orders":[]
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}
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try:
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with open(METRICS_FILE,"w") as f: json.dump(data,f,default=str)
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except IOError: pass
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# ═══════════════════════ Signals ═══════════════════════
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def compute_signals():
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if len(btc_prices)<20 or len(eth_prices)<10: return
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btc = btc_prices[-1]; eth = eth_prices[-1]
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# OFI: 5-tick reversal
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if len(btc_prices)>=5:
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ret = (btc-btc_prices[-5])/btc_prices[-5]
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if ret>0.0004: STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"SELL","strength":ret})
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elif ret<-0.0004: STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(ret)})
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# Iceberg: trend count
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if len(btc_prices)>=10:
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up = sum(1 for i in range(-9,0) if btc_prices[i+1]>btc_prices[i])
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if up>=5: STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"BUY","strength":up/10})
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elif up<=5: STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"SELL","strength":1-up/10})
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# Funding Rate Arb: real API data
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try:
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from strategies.funding_arb import get_funding_rates
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rates = get_funding_rates(use_testnet=True)
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annual_rate = rates.get("BTC", 0)
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if abs(annual_rate) > 0.03: # >3% APR threshold (testnet: lower liquidity = lower threshold)
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sig = "SELL" if annual_rate > 0 else "BUY"
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STRATEGIES["Funding Rate Arb"]["signals"].append({
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"time":time.time(), "signal":sig,
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"strength": min(1.0, abs(annual_rate) * 10),
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"reason": f"funding_{annual_rate*100:.1f}pct_apr"
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})
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except Exception:
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# Fallback: use price proxy if module unavailable
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if len(btc_prices)>=20:
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rate = (btc/btc_prices[-20]-1)/20
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if abs(rate)>0.0005:
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STRATEGIES["Funding Rate Arb"]["signals"].append({"time":time.time(),"signal":"SELL" if rate>0 else "BUY","strength":abs(rate)*10000})
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# Pairs: ratio Z-score
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if len(btc_prices)>=20 and len(eth_prices)>=20:
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ratios = [btc_prices[i]/eth_prices[i] for i in range(-20,0)]
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mu = sum(ratios)/len(ratios)
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std = math.sqrt(sum((r-mu)**2 for r in ratios)/len(ratios))
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cur = btc/eth if eth>0 else 0
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if std>0:
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z = (cur-mu)/std
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if z>1.5: STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"SELL_ETH","strength":z})
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elif z<-1.5: STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"BUY_ETH","strength":abs(z)})
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# Kalman Pairs: adaptive hedge via Kalman filter (falls back to Pairs logic)
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if len(btc_prices)>=20 and len(eth_prices)>=20:
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try:
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from strategies.kalman_pairs import KalmanPairsTrader
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if "_kalman_live" not in dir():
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globals()["_kalman_live"] = KalmanPairsTrader(
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transition_covariance=1e-4, observation_covariance=1e-2,
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z_entry=2.0, z_exit=0.5, warmup_bars=20,
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)
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result = globals()["_kalman_live"].step(eth, btc)
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if result["signal"] != 0:
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sig = "BUY_ETH" if result["signal"] > 0 else "SELL_ETH"
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STRATEGIES["Kalman Pairs"]["signals"].append({
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"time":time.time(), "signal":sig,
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"strength":abs(result["z_score"])
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})
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except: pass
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# Momentum: Bollinger
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if len(btc_prices)>=20:
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w = list(btc_prices)[-20:]; sma = sum(w)/len(w)
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variance = sum((p-sma)**2 for p in w)/len(w); std = math.sqrt(variance)
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if std>0:
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if btc > sma+1.5*std: STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"BUY","strength":(btc-sma-2*std)/std})
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elif btc < sma-1.5*std: STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"SELL","strength":(sma-2*std-btc)/std})
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# Mean Reversion: VWAP
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if len(btc_prices)>=20:
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w = list(btc_prices)[-20:]; vols = [1+i/len(w) for i in range(len(w))]
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vwap = sum(p*v for p,v in zip(w,vols))/sum(vols)
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vstd = math.sqrt(sum((p-vwap)**2 for p in w)/len(w))
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dev = (btc-vwap)/vstd if vstd>0 else 0
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if dev>1.0: STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"SELL","strength":dev})
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elif dev<-1.0: STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(dev)})
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# Trim signals
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for s in STRATEGIES.values(): s["signals"] = s["signals"][-20:]
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# ═══════════════════════ Main ═══════════════════════
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async def main():
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private_key = load_key()
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if not private_key: log.error("No key"); sys.exit(1)
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client = HyperliquidHttpClient(private_key=private_key, vault_address=None, environment=HyperliquidEnvironment.TESTNET)
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addr = client.get_user_address()
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client.set_account_id("HYPERLIQUID-"+addr)
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# Load instrument definitions — try testnet SDK first, fallback to raw APIs
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insts = []; perps = {}
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try:
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insts = await client.load_instrument_definitions(include_perps=True)
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perps = {str(i.id.symbol): i for i in insts if "PERP" in str(i.id.symbol)}
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for inst in perps.values(): client.cache_instrument(inst)
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except Exception as e:
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log.warning(f"SDK instrument load failed: {e}")
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if not perps:
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log.info("Loading perps from mainnet API directly...")
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try:
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meta_r = requests.post(TESTNET_API, json={"type":"meta"}, timeout=10)
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meta = meta_r.json()
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for asset in meta.get("universe", []):
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name = asset.get("name", "")
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if name:
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# Build a minimal perp-like object for our purposes
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perps[name] = type('Perp', (), {
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'id': type('ID', (), {'symbol': name})(),
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'base': name,
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'quote': 'USD',
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})()
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log.info(f"Loaded {len(perps)} perps from mainnet meta")
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except Exception as e:
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log.error(f"Mainnet meta fallback failed: {e}")
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if perps:
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log.info(f"Perps available: {list(perps.keys())[:10]}...")
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else:
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log.error("No perps loaded — cannot continue")
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sys.exit(1)
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# Find BTC/ETH perps dynamically (testnet IDs may differ from mainnet)
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btc_perp = None; eth_perp = None
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for k, v in perps.items():
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ku = k.upper()
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if btc_perp is None and ("BTC" in ku):
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btc_perp = v
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if eth_perp is None and ("ETH" in ku):
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eth_perp = v
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if not btc_perp or not eth_perp:
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log.error(f"Could not find BTC/ETH perps. Available: {list(perps.keys())[:10]}")
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sys.exit(1)
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prices = get_mark_prices()
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btc_bid, btc_ask, btc_mid = get_orderbook("BTC")
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eth_bid, eth_ask, eth_mid = get_orderbook("ETH")
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log.info("="*60)
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log.info(" FTDT Quant Lab — QUOTING AT BEST BID/ASK")
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log.info(f" Wallet: {addr}")
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log.info(f" BTC: bid=${btc_bid:,.0f} ask=${btc_ask:,.0f} (spread=${btc_ask-btc_bid:.1f})")
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log.info(f" ETH: bid=${eth_bid:,.0f} ask=${eth_ask:,.0f} (spread=${eth_ask-eth_bid:.1f})")
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log.info(f" Mode: POST-ONLY at best bid/ask | Maker: 0.02%")
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log.info(f" 7 strategies | A-S is DUAL-SIDED quoting")
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log.info(f" Dashboard: https://ftdt.io/cv")
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log.info("="*60)
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# Cancel stale
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open_ords = requests.post(TESTNET_API, json={"type":"openOrders","user":addr}, timeout=10).json()
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for o in open_ords:
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try:
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iid = InstrumentId.from_str(f"{o['coin']}-USD-PERP.HYPERLIQUID")
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client.cancel_order(instrument_id=iid, client_order_id=ClientOrderId(o["cloid"]))
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except: pass
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log.info(f"Cleared {len(open_ords)} stale orders")
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existing = get_fills(addr)
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for f in existing: seen_fills.add(f.get("tid",0))
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log.info(f"Tracking {len(seen_fills)} existing fills")
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for s in STRATEGIES.values(): s["status"]="running"
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for name in STRATEGIES: strategy_equity[name]=[]
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write_metrics(addr)
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tick=0; names=list(STRATEGIES.keys()); idx=0
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try:
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while True:
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tick+=1
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prices = get_mark_prices()
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btc = prices.get("BTC",0); eth = prices.get("ETH",0)
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if btc>0: btc_prices.append(btc)
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if eth>0: eth_prices.append(eth)
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# Process fills
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fills = get_fills(addr); new_fills=0
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for f in fills:
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tid=f.get("tid",0)
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if tid in seen_fills: continue
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seen_fills.add(tid)
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side=f.get("side",""); sz=float(f.get("sz",0)); px=float(f.get("px",0))
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closed_pnl=float(f.get("closedPnl",0)); fee=float(f.get("fee","0"))
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# Attribute fill by size (now unique per strategy)
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strat=None
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for n,cfg in STRATEGIES.items():
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if abs(sz-cfg["size"])<0.000001:
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strat=n
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break
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if not strat: continue
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net=closed_pnl-abs(fee)
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STRATEGIES[strat]["pnl"]+=net; STRATEGIES[strat]["trades_today"]+=1
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STRATEGIES[strat]["fee_paid"]+=abs(fee)
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if closed_pnl>0: STRATEGIES[strat]["wins"]+=1
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STRATEGIES[strat]["pnl_pct"]=STRATEGIES[strat]["pnl"]/STRATEGIES[strat]["allocation"]*100
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strategy_equity[strat].append({"t":time.time(),"v":STRATEGIES[strat]["allocation"]+STRATEGIES[strat]["pnl"]})
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trades_log.append({"time":datetime.now().strftime("%H:%M:%S"),"strategy":strat,"side":"BUY" if side=="B" else "SELL","size":sz,"price":px,"pnl":round(net,4),"fee":round(abs(fee),4)})
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new_fills+=1
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# Signals every 5 ticks
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if tick%5==0: compute_signals()
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# Execute ALL strategies every 4 seconds
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if tick>=3 and tick%4==0:
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btc_bid, btc_ask, btc_mid = get_orderbook("BTC")
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try:
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eth_bid, eth_ask, eth_mid = get_orderbook("ETH")
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except Exception as e:
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eth_bid = eth_ask = eth_mid = 0
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if btc_bid<=0 or btc_ask<=0: continue
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for name in names:
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cfg=STRATEGIES[name]
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coin="BTC" if "BTC" in cfg["instrument"] else "ETH"
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perp=btc_perp if coin=="BTC" else eth_perp
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bid=btc_bid if coin=="BTC" else eth_bid
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ask=btc_ask if coin=="BTC" else eth_ask
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mid=btc_mid if coin=="BTC" else eth_mid
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if bid<=0 or ask<=0: continue
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# Check if this strategy has a position; skip if already filled
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has_position = name in active_cloids and tick - active_cloids_times.get(name,0) < 60
|
||
|
||
# Determine signal
|
||
signal=None
|
||
if cfg["signals"]:
|
||
latest = cfg["signals"][-1]
|
||
# Only use recent signals (< 10 seconds old)
|
||
if time.time() - latest["time"] < 10:
|
||
signal=latest["signal"]
|
||
|
||
# Close on opposing signal
|
||
if has_position and signal:
|
||
prev_signal = active_cloids.get(name,"")
|
||
if ("BUY" in str(signal).upper() and "SELL" in str(prev_signal).upper()) or ("SELL" in str(signal).upper() and "BUY" in str(prev_signal).upper()):
|
||
try:
|
||
client.cancel_order(instrument_id=perp.id, client_order_id=ClientOrderId(active_cloids[name]))
|
||
except: pass
|
||
del active_cloids[name]
|
||
has_position = False
|
||
|
||
# Take-profit: close if price moved 2x fee in our favor
|
||
if has_position:
|
||
entry_px = active_cloids_px.get(name, 0)
|
||
if entry_px > 0:
|
||
if "BUY" in str(active_cloids[name]).upper() and mid > entry_px * 1.001:
|
||
try:
|
||
client.cancel_order(instrument_id=perp.id, client_order_id=ClientOrderId(active_cloids[name]))
|
||
except: pass
|
||
del active_cloids[name]
|
||
has_position = False
|
||
elif "SELL" in str(active_cloids[name]).upper() and mid < entry_px * 0.999:
|
||
try:
|
||
client.cancel_order(instrument_id=perp.id, client_order_id=ClientOrderId(active_cloids[name]))
|
||
except: pass
|
||
del active_cloids[name]
|
||
has_position = False
|
||
|
||
if has_position: continue # Don't replace existing orders
|
||
|
||
# Avellaneda-Stoikov: DUAL-SIDED (always active)
|
||
if name=="Avellaneda-Stoikov":
|
||
cid_bid=ClientOrderId(str(UUID4())); cid_ask=ClientOrderId(str(UUID4()))
|
||
try:
|
||
client.submit_order(instrument_id=perp.id,client_order_id=cid_bid,order_side=OrderSide.BUY,order_type=OrderType.LIMIT,quantity=Quantity.from_str(str(cfg["size"])),price=Price.from_str(str(int(bid))),time_in_force=TimeInForce.GTC,post_only=True)
|
||
client.submit_order(instrument_id=perp.id,client_order_id=cid_ask,order_side=OrderSide.SELL,order_type=OrderType.LIMIT,quantity=Quantity.from_str(str(cfg["size"])),price=Price.from_str(str(int(ask))),time_in_force=TimeInForce.GTC,post_only=True)
|
||
if tick%60==0:
|
||
log.info(f"[Avel] DUAL: BID {cfg['size']} @ ${int(bid):,} | ASK {cfg['size']} @ ${int(ask):,}")
|
||
active_cloids[name]=str(cid_bid)
|
||
active_cloids_times[name]=tick
|
||
active_cloids_px[name]=bid
|
||
except Exception as e: pass
|
||
continue
|
||
|
||
# For signal-driven strategies: use aggressive offset
|
||
if signal:
|
||
side=OrderSide.SELL if "SELL" in str(signal).upper() else OrderSide.BUY
|
||
# Aggressive: 0.03% inside the spread for higher fill probability
|
||
offset = int(mid * 0.0003)
|
||
px_level = ask - offset if side==OrderSide.SELL else bid + offset
|
||
px_level = max(px_level, 1)
|
||
else:
|
||
# No signal/default: skip (don't random-trade)
|
||
continue
|
||
|
||
if px_level<=0: continue
|
||
|
||
cid=ClientOrderId(str(UUID4()))
|
||
try:
|
||
client.submit_order(instrument_id=perp.id,client_order_id=cid,order_side=side,order_type=OrderType.LIMIT,quantity=Quantity.from_str(str(cfg["size"])),price=Price.from_str(str(int(px_level))),time_in_force=TimeInForce.GTC,post_only=True)
|
||
if tick%60==0:
|
||
side_str="BUY" if side==OrderSide.BUY else "SELL"
|
||
log.info(f"[{name[:4]:4s}] {side_str} {cfg['size']} @ ${int(px_level):,} ({'best bid '+str(int(bid)) if side==OrderSide.BUY else 'best ask '+str(int(ask))})")
|
||
active_cloids[name]=str(cid)
|
||
active_cloids_times[name]=tick
|
||
active_cloids_px[name]=px_level
|
||
except Exception as e:
|
||
err=str(e)
|
||
if "would have immediately matched" in err or "cross" in err.lower():
|
||
cid2=ClientOrderId(str(UUID4()))
|
||
try:
|
||
client.submit_order(instrument_id=perp.id,client_order_id=cid2,order_side=side,order_type=OrderType.LIMIT,quantity=Quantity.from_str(str(cfg["size"])),price=Price.from_str(str(int(px_level))),time_in_force=TimeInForce.IOC)
|
||
active_cloids[name]=str(cid2)
|
||
active_cloids_times[name]=tick
|
||
active_cloids_px[name]=px_level
|
||
except: pass
|
||
|
||
# Equity
|
||
tp=sum(s["pnl"] for s in STRATEGIES.values())
|
||
if tick%2==0: equity_history.append({"t":time.time(),"v":TOTAL_EQUITY+tp})
|
||
write_metrics(addr)
|
||
|
||
if tick%20==0:
|
||
tp=sum(s["pnl"] for s in STRATEGIES.values())
|
||
tr=sum(s["trades_today"] for s in STRATEGIES.values())
|
||
tf=sum(s["fee_paid"] for s in STRATEGIES.values())
|
||
log.info(f"Tick {tick:4d} | PnL: ${tp:+.2f} | Trades: {tr:3d} | Fees: ${tf:.4f} | New fills: {new_fills}")
|
||
|
||
await asyncio.sleep(1)
|
||
except KeyboardInterrupt: log.info("Stopping...")
|
||
|
||
# Cancel all
|
||
open_ords = requests.post(TESTNET_API, json={"type":"openOrders","user":addr}, timeout=10).json()
|
||
for o in open_ords:
|
||
try:
|
||
iid=InstrumentId.from_str(f"{o['coin']}-USD-PERP.HYPERLIQUID")
|
||
client.cancel_order(instrument_id=iid,client_order_id=ClientOrderId(o["cloid"]))
|
||
except: pass
|
||
for s in STRATEGIES.values(): s["status"]="idle"
|
||
write_metrics(addr)
|
||
tf=sum(s["fee_paid"] for s in STRATEGIES.values())
|
||
tp=sum(s["pnl"] for s in STRATEGIES.values())
|
||
log.info(f"Stopped. PnL: ${tp:+.2f}, Fees: ${tf:.4f}")
|
||
|
||
if __name__=="__main__": asyncio.run(main())
|