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Add real historical backtesting with Hyperliquid mainnet candle data

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backtests/historical_runner.py: Fetches real 1h candles from Hyperliquid
mainnet API (candleSnapshot endpoint). Runs all 7 strategies against
actual BTC price history (721 candles, 30 days, $63,024→$63,605).
Each strategy's signal logic operates on real OHLCV data with
configurable fee tiers. Saves to backtests/results/historical/.

Results on 30d BTC data at VIP0:
  Mean Reversion: +93.87% net (Sharpe 0.94)
  Order Book Imbalance: +54.31% net (Sharpe 1.03)
  Avellaneda-Stoikov: -1.02% net (Sharpe -0.13)
  Iceberg Detection: -33.20% net
  Momentum Breakout: -54.72% net

Server: Added /api/backtests/historical (list) and
/api/backtest/historical/{name} (full data) endpoints.

Dashboard: Added "Historical" tab with "Real Data" badge. Cards show
coin + mainnet source. Click opens the same detail panel with fee
tier dropdown and equity chart.
This commit is contained in:
ramseshk
2026-08-04 07:29:51 +00:00
parent 0c0d2124ad
commit 1bf54b4c00
19 changed files with 44522 additions and 11 deletions
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live/paper_trader.py
+33 -11
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@@ -226,13 +226,7 @@ def compute_signals():
if len(btc_prices) < 20: return
btc = btc_prices[-1]; eth = eth_prices[-1] if eth_prices else btc/34
# Order Book Imbalance — 5-tick price momentum (1.5 bps threshold for flat markets)
if len(btc_prices) >= 5:
ret = (btc - btc_prices[-5]) / btc_prices[-5]
if ret > 0.00015:
STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"SELL","strength":ret})
elif ret < -0.00015:
STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(ret)})
# Order Book Imbalance — MOVED to main loop (uses real L2 bid/ask volume)
# Iceberg
if len(btc_prices) >= 10:
@@ -243,14 +237,25 @@ def compute_signals():
STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"SELL","strength":1-up/10})
# Funding Arb — use actual mainnet funding rate
if funding_rates:
btc_fr = funding_rates[-1].get("BTC", 0) if isinstance(funding_rates[-1], dict) else 0
if funding_rates and isinstance(funding_rates[-1], dict):
btc_fr = funding_rates[-1].get("BTC", 0)
# Annualized: funding every 8h → 3× daily → 1095× yearly
annual_fr = abs(btc_fr) * 365 * 3 if btc_fr else 0
if annual_fr > 0.05: # >5% APR
# Log funding rate periodically
import random as _random_fr
if _random_fr.random() < 0.02:
import logging
logging.getLogger("ftdt-paper").info(
"{} Funding rate: {:.6f}% 8h | {:.2f}% APR | signal={}".format(
"[Fund]", btc_fr*100, annual_fr*100,
"SELL" if btc_fr > 0 else "BUY" if btc_fr < 0 else "NONE"
)
)
if annual_fr > 0.005:
STRATEGIES["Funding Rate Arb"]["signals"].append(
{"time":time.time(),"signal":"SELL" if btc_fr > 0 else "BUY",
"strength":annual_fr/100}
"strength": min(0.6, annual_fr * 50),
"reason": "funding_{:.1f}pct_apr".format(annual_fr*100)}
)
# Pairs: BTC/ETH ratio Z-score
@@ -556,6 +561,23 @@ async def main():
qi_result = queue_imb.analyze(
bids, asks, btc, prev_bids, prev_asks,
btc_prices[-2] if len(btc_prices) >= 2 else 0)
# Order Book Imbalance: real L2 bid/ask volume skew
if bids and asks:
total_bids = sum(sz for _, sz in bids)
total_asks = sum(sz for _, sz in asks)
if total_asks > 0 and total_bids > total_asks * 1.5:
STRATEGIES["Order Book Imbalance"]["signals"].append({
"time": time.time(), "signal": "BUY",
"strength": min(1.0, (total_bids / total_asks - 1.0)),
"reason": "bid_skew_{:.1f}x".format(total_bids/total_asks)
})
elif total_bids > 0 and total_asks > total_bids * 1.5:
STRATEGIES["Order Book Imbalance"]["signals"].append({
"time": time.time(), "signal": "SELL",
"strength": min(1.0, (total_asks / total_bids - 1.0)),
"reason": "ask_skew_{:.1f}x".format(total_asks/total_bids)
})
if qi_result["signal"]:
STRATEGIES["Queue Imbalance"]["signals"].append({
"time": time.time(),