ramseshk
1ece7ec7c6
Paper trader now tracks individual equity history per strategy (strategy_equity dict with deque per strategy). Metrics file exports per-strategy data for dashboard rendering. Dashboard paper chart upgraded to 7 overlaid area series: - Each strategy gets its own colored curve (green, blue, purple, etc.) - 300px height for better visibility of multiple lines - Color palette distinguishes strategies at a glance $100K total capital: $10K per strategy × 7 + $30K reserve. Exeria Charts evaluated: excellent library (Benzinga award winner, Canvas/WebGL, exchange connectors) but requires npm+bundler — not suitable for single-file dashboard. Lightweight-charts remains the right choice for our architecture.
464 lines
20 KiB
Python
464 lines
20 KiB
Python
"""
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Paper trading engine — runs strategies against HYPERLIQUID MAINNET data.
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Pulls real mainnet prices, orderbooks, and funding rates every second.
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Executes all 7 strategies in simulation mode — tracks virtual positions,
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computes PnL with realistic fees and slippage. No real orders.
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Writes to /tmp/ftdt-paper-metrics.json for the dashboard.
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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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logging.basicConfig(level=logging.INFO, format="%(asctime)s [paper] %(message)s", datefmt="%H:%M:%S")
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log = logging.getLogger("ftdt-paper")
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# ═══════════════════════ Config ═══════════════════════
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MAINNET_API = "https://api.hyperliquid.xyz/info"
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METRICS_FILE = "/tmp/ftdt-paper-metrics.json"
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STARTING_CAPITAL = 100000.0 # $100,000 paper trading capital
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RESERVE = 30000.0
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TAKER_FEE = 0.0005 # 5 bps taker (realistic for paper fills)
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SLIPPAGE_BPS = 1.0 # 1 bps slippage
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# ═══════════════════════ Strategy state ═══════════════════════
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STRATEGIES = {
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"Order Book Imbalance": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "reversal", "size": 0.002,
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"description": "L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate. Mean-reverting at volume extremes.",
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},
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"Iceberg Detection": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "momentum", "size": 0.001,
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"description": "Detects whale accumulation (many small buys over time). Follows the smart money flow.",
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},
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"Funding Rate Arb": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "carry", "size": 0.005,
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"description": "Delta-neutral carry trade — shorts perp when funding rate is high, collects hourly payments.",
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},
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"Pairs Trading": {
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"allocation": 10000.0, "instrument": "ETH", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "stat_arb", "size": 0.05,
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"description": "BTC/ETH spread mean reversion — trades when Z-score exceeds 1.5 sigma. Pairs converge back to equilibrium.",
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},
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"Avellaneda-Stoikov": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "market_making", "size": 0.001,
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"description": "Dual-sided quoting at best bid/ask — captures spread via stochastic control. Simulated fill when spread is crossed.",
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},
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"Momentum Breakout": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "momentum", "size": 0.002,
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"description": "Bollinger Band (2σ) breakout — enters when price breaks bands with volume confirmation.",
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},
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"Mean Reversion": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "reversal", "size": 0.002,
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"description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
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},
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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 = {name: deque(maxlen=300) for name in STRATEGIES}
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btc_prices: deque = deque(maxlen=120)
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eth_prices: deque = deque(maxlen=120)
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funding_rates: deque = deque(maxlen=100)
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# ═══════════════════════ Mainnet Data ═══════════════════════
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def get_mainnet_prices():
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"""Get mark prices from mainnet."""
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try:
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r = requests.post(MAINNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
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data = r.json()
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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 as e:
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log.warning(f"Mainnet price error: {e}")
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return {}
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def get_mainnet_funding():
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"""Get funding rates from mainnet."""
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try:
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r = requests.post(MAINNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
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data = r.json()
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rates = {}
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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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rates[u["name"]] = float(data[1][i].get("funding", 0))
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return rates
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except:
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return {}
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def get_mainnet_orderbook(coin):
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"""Get L2 orderbook from mainnet."""
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try:
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r = requests.post(MAINNET_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
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except: return 0,0
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# ═══════════════════════ Signal Engine ═══════════════════════
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def compute_signals():
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if len(btc_prices) < 20: return
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btc = btc_prices[-1]; eth = eth_prices[-1] if eth_prices else btc/34
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# OFI
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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 > 0.0005:
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STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"SELL","strength":ret})
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elif ret < -0.0005:
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STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(ret)})
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# Iceberg
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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 >= 7:
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STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"BUY","strength":up/10})
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elif up <= 3:
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STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"SELL","strength":1-up/10})
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# Funding Arb — use actual mainnet funding rate
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if funding_rates:
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btc_fr = funding_rates[-1].get("BTC", 0) if isinstance(funding_rates[-1], dict) else 0
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# Annualized: funding every 8h → 3× daily → 1095× yearly
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annual_fr = abs(btc_fr) * 365 * 3 if btc_fr else 0
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if annual_fr > 0.05: # >5% APR
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STRATEGIES["Funding Rate Arb"]["signals"].append(
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{"time":time.time(),"signal":"SELL" if btc_fr > 0 else "BUY",
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"strength":annual_fr/100}
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)
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# Pairs: BTC/ETH 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] / max(eth_prices[i], 0.01) 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 / max(eth, 0.01)
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if std > 0:
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z = (cur - mu) / std
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if z > 1.5:
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STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"SELL_ETH","strength":z})
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elif z < -1.5:
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STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"BUY_ETH","strength":abs(z)})
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# Momentum Breakout
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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 + 2*std:
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STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"BUY","strength":(btc-sma-2*std)/std})
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elif btc < sma - 2*std:
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STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"SELL","strength":(sma-2*std-btc)/std})
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# Mean Reversion
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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 > 1.5:
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STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"SELL","strength":dev})
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elif dev < -1.5:
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STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(dev)})
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for s in STRATEGIES.values():
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s["signals"] = s["signals"][-20:]
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# ═══════════════════════ Fill Simulation ═══════════════════════
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def simulate_fill(name: str, side: str, coin: str, price: float):
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"""Simulate a trade fill at market price with fees."""
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cfg = STRATEGIES[name]
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sz = cfg["size"]
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notional = sz * price
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fee = notional * TAKER_FEE
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slippage = notional * SLIPPAGE_BPS / 10000
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cfg["fee_paid"] += fee
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if side == "BUY":
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# Opening or adding long
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if cfg["position"] <= 0:
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# Close short if any
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if cfg["position"] < 0:
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# PnL from closing short
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close_pnl = abs(cfg["position"]) * (cfg["entry_price"] - price)
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cfg["pnl"] += close_pnl
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cfg["entry_price"] = 0
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cfg["position"] = 0
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if close_pnl > 0: cfg["wins"] += 1
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": name, "side": "BUY (close short)",
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"size": abs(cfg["position"] if cfg["position"] < 0 else sz),
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"price": price, "pnl": round(close_pnl - fee - slippage, 4),
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"fee": round(fee, 4),
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})
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# Open long
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cfg["entry_price"] = price
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cfg["position"] = sz
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else:
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# Adding to long
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cfg["entry_price"] = (cfg["entry_price"] * cfg["position"] + price * sz) / (cfg["position"] + sz)
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cfg["position"] += sz
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cfg["pnl"] -= fee + slippage
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else: # SELL
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if cfg["position"] >= 0:
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if cfg["position"] > 0:
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close_pnl = cfg["position"] * (price - cfg["entry_price"])
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cfg["pnl"] += close_pnl
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cfg["entry_price"] = 0
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cfg["position"] = 0
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if close_pnl > 0: cfg["wins"] += 1
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": name, "side": "SELL (close long)",
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"size": sz,
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"price": price, "pnl": round(close_pnl - fee - slippage, 4),
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"fee": round(fee, 4),
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})
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cfg["entry_price"] = price
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cfg["position"] = -sz
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else:
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cfg["entry_price"] = (cfg["entry_price"] * abs(cfg["position"]) + price * sz) / (abs(cfg["position"]) + sz)
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cfg["position"] -= sz
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cfg["pnl"] -= fee + slippage
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cfg["trades_today"] += 1
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cfg["pnl_pct"] = cfg["pnl"] / cfg["allocation"] * 100
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# Track per-strategy equity
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strategy_equity[name].append({"t": time.time(), "v": cfg["allocation"] + cfg["pnl"]})
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# ═══════════════════════ A-S Spread Capture ═══════════════════════
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def simulate_avellaneda(btc_bid, btc_ask):
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"""Avellaneda-Stoikov: simulate spread capture when orders are at best bid/ask."""
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cfg = STRATEGIES["Avellaneda-Stoikov"]
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if btc_bid <= 0 or btc_ask <= 0:
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return
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# Each tick, there's a chance our quotes get hit
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# On mainnet, this happens frequently. Simulate with probability.
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if random.random() < 0.15: # 15% per tick = fill every ~7 seconds on average
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# Our bid gets hit (we buy at bid, sell at ask later for profit)
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if cfg["position"] <= 0:
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# Buy at bid
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bid_fill_price = btc_bid
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else:
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# Sell at ask (close position)
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bid_fill_price = btc_ask
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side = "BUY" if cfg["position"] <= 0 else "SELL"
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sz = cfg["size"]
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notional = sz * bid_fill_price
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fee = notional * TAKER_FEE
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spread_profit = sz * (btc_ask - btc_bid)/2 if side == "BUY" else 0
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if side == "BUY":
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if cfg["position"] < 0:
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close_pnl = abs(cfg["position"]) * (cfg["entry_price"] - bid_fill_price)
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cfg["pnl"] += close_pnl
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if close_pnl > 0: cfg["wins"] += 1
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cfg["entry_price"] = bid_fill_price
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cfg["position"] = sz
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cfg["pnl"] += spread_profit - fee
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else:
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if cfg["position"] > 0:
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close_pnl = cfg["position"] * (bid_fill_price - cfg["entry_price"])
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cfg["pnl"] += close_pnl
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if close_pnl > 0: cfg["wins"] += 1
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": "Avellaneda-Stoikov",
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"side": "SELL", "size": sz,
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"price": bid_fill_price,
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"pnl": round(close_pnl - fee, 4),
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"fee": round(fee, 4),
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})
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cfg["position"] = 0
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cfg["entry_price"] = 0
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cfg["fee_paid"] += fee
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cfg["trades_today"] += 1
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cfg["pnl_pct"] = cfg["pnl"] / cfg["allocation"] * 100
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strategy_equity["Avellaneda-Stoikov"].append({"t": time.time(), "v": cfg["allocation"] + cfg["pnl"]})
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# ═══════════════════════ Metrics ═══════════════════════
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def write_metrics():
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total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
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total_pnl_pct = (total_pnl / (STARTING_CAPITAL-RESERVE)) * 100 if STARTING_CAPITAL > RESERVE else 0
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for s in STRATEGIES.values():
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if s["trades_today"] > 0:
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s["win_rate"] = s["wins"] / s["trades_today"]
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data = {
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"timestamp": time.time(),
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"mode": "paper",
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"source": "Hyperliquid Mainnet",
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"total_equity": STARTING_CAPITAL + total_pnl,
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"base_equity": STARTING_CAPITAL,
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"total_pnl": total_pnl,
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"total_pnl_pct": total_pnl_pct,
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"reserve": RESERVE,
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"equity_history": equity_history[-600:],
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"strategy_equity": {k: list(v)[-300:] for k, v in strategy_equity.items()},
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"strategies": STRATEGIES,
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"trades": trades_log[-200:],
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"status": "running",
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"btc_price": btc_prices[-1] if btc_prices else 0,
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"eth_price": eth_prices[-1] if eth_prices else 0,
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}
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try:
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with open(METRICS_FILE, "w") as f:
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json.dump(data, f, default=str)
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except IOError: pass
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# ═══════════════════════ Main ═══════════════════════
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async def main():
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log.info("="*60)
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log.info(" FTDT Quant Lab — PAPER TRADING (Mainnet Data)")
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log.info(f" Capital: ${STARTING_CAPITAL:,} | Reserve: ${RESERVE:,}")
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log.info(f" 7 strategies × $1,000 allocation")
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log.info(f" Fees: {TAKER_FEE*100:.2f}% taker | Slippage: {SLIPPAGE_BPS} bps")
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log.info(f" Data: Hyperliquid MAINNET")
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log.info(f" Dashboard: https://ftdt.io/cv")
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log.info("="*60)
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for s in STRATEGIES.values():
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s["status"] = "running"
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write_metrics()
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tick = 0
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strategy_names = list(STRATEGIES.keys())
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idx = 0
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try:
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while True:
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tick += 1
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# Fetch mainnet data
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if tick % 2 == 0: # Every 2 seconds to respect rate limits
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prices = get_mainnet_prices()
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btc = prices.get("BTC", 0)
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eth = prices.get("ETH", 0)
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if btc > 0:
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btc_prices.append(btc)
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if eth > 0:
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eth_prices.append(eth)
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# Funding rates every 10 seconds
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if tick % 10 == 0:
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fr = get_mainnet_funding()
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if fr:
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funding_rates.append(fr)
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# Compute signals every 5 ticks
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if tick % 5 == 0:
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compute_signals()
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# Execute signals every 3-5 ticks
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if tick >= 10 and tick % random.randint(3, 6) == 0:
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btc = btc_prices[-1] if btc_prices else 0
|
||
eth = eth_prices[-1] if eth_prices else 0
|
||
if btc <= 0: continue
|
||
|
||
# Get orderbook for A-S
|
||
btc_bid, btc_ask = get_mainnet_orderbook("BTC")
|
||
|
||
# Avellaneda-Stoikov: simulate spread capture
|
||
simulate_avellaneda(btc_bid, btc_ask)
|
||
|
||
# Process next strategy's signals
|
||
name = strategy_names[idx % 7]
|
||
idx += 1
|
||
cfg = STRATEGIES[name]
|
||
if name == "Avellaneda-Stoikov":
|
||
continue # Already handled above
|
||
|
||
# Check for signals
|
||
if not cfg["signals"]:
|
||
continue
|
||
|
||
sig = cfg["signals"][-1]
|
||
signal_str = str(sig["signal"])
|
||
|
||
coin = cfg["instrument"]
|
||
px = btc if coin == "BTC" else eth
|
||
if px <= 0: continue
|
||
|
||
if "BUY" in signal_str.upper():
|
||
simulate_fill(name, "BUY", coin, px)
|
||
log.info(f"[{name[:4]:4s}] PAPER BUY {cfg['size']} {coin} @ ${px:,.1f} | PnL: ${cfg['pnl']:+.2f}")
|
||
elif "SELL" in signal_str.upper():
|
||
simulate_fill(name, "SELL", coin, px)
|
||
log.info(f"[{name[:4]:4s}] PAPER SELL {cfg['size']} {coin} @ ${px:,.1f} | PnL: ${cfg['pnl']:+.2f}")
|
||
|
||
# Equity history
|
||
total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
|
||
if tick % 3 == 0:
|
||
equity_history.append({"t": time.time(), "v": STARTING_CAPITAL + total_pnl})
|
||
|
||
write_metrics()
|
||
|
||
if tick % 30 == 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())
|
||
btc_now = btc_prices[-1] if btc_prices else 0
|
||
log.info(
|
||
f"Tick {tick:4d} | BTC: ${btc_now:,.0f} | "
|
||
f"PnL: ${tp:+.2f} | Trades: {tr:3d} | Fees: ${tf:.2f}"
|
||
)
|
||
|
||
await asyncio.sleep(1)
|
||
|
||
except KeyboardInterrupt:
|
||
log.info("Stopping paper trader...")
|
||
|
||
for s in STRATEGIES.values():
|
||
s["status"] = "idle"
|
||
write_metrics()
|
||
tp = sum(s["pnl"] for s in STRATEGIES.values())
|
||
tr = sum(s["trades_today"] for s in STRATEGIES.values())
|
||
log.info(f"Paper trading stopped. Final PnL: ${tp:+.2f}, Trades: {tr}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(main())
|