feat: funding arb strategy, queue-aware paper fills, WQI live integration
- strategies/funding_arb_strategy.py: full backtestable funding rate carry module with entry/exit thresholds, position tracking, funding payment accounting, basis stop-loss, max-hold timeout. Includes backtest_funding_arb() and run_funding_discovery() for threshold optimization - live/node_v2.py: replaced naive random fills with QueueAwareFillModel (sim/fills.py) with queue-priority simulation; integrated WQI predictor and funding arb strategies; per-coin WQI signal generation every 3 ticks; funding arb metrics in dashboard - cli.py: added 'funding' command for funding rate distribution analysis and threshold backtesting - tests/test_funding_arb.py: 20 tests covering entry/exit logic, fee accounting, signal generation, backtesting, and node integration 321 tests passing (20 new).
This commit is contained in:
ramseshk
@@ -499,6 +499,52 @@ def cmd_discover(args):
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print(f"\nPipeline ready. Run 'python -m cli tick' to backtest strategies on this data.")
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def cmd_funding(args):
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"""Funding rate arb discovery — analyze historical funding rates and run backtests."""
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from strategies.funding_arb_strategy import run_funding_discovery
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import json as _json
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result = run_funding_discovery(
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data_dir=args.data_dir,
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coin=args.coin,
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start_date=args.start_date,
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end_date=args.end_date,
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)
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if "error" in result:
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print(f"Error: {result['error']}")
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return
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print(f"\n{'═' * 60}")
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print(f" Funding Rate Analysis — {args.coin}")
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print(f" {result['n_observations']:,} observations")
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print(f"{'═' * 60}")
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dist = result["rate_distribution"]
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print(f"\n Rate Distribution (annualized):")
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print(f" Mean: {dist['mean_apr_pct']:>8.2f}%")
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print(f" Std: {dist['std_apr_pct']:>8.2f}%")
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print(f" Max: {dist['max_apr_pct']:>8.2f}%")
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print(f" Min: {dist['min_apr_pct']:>8.2f}%")
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print(f"\n Absolute Rate Percentiles:")
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for k, v in dist["abs_percentiles"].items():
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print(f" {k}: {v:>8.2f}%")
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print(f"\n{'─' * 60}")
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print(f" Backtest Results by Threshold:")
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print(f" {'Threshold':>12s} {'Trades':>7s} {'Win Rate':>9s} "
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f"{'Net PnL':>10s} {'Avg PnL':>10s} {'Avg Hold':>9s}")
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print(f" {'─' * 60}")
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for label, bt in result.get("backtests", {}).items():
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print(f" {label:>12s} {bt['total_trades']:>7d} "
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f"{bt['win_rate']:>8.1%} "
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f"${bt['total_net_pnl']:>9.4f} ${bt['avg_net_pnl']:>9.4f} "
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f"{bt['avg_hold_hours']:>8.1f}h")
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print(f"\n Run 'python -m cli collect --mainnet' to gather data.")
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print(f" Then 'python -m cli funding --coin BTC' to re-run.")
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def main():
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import argparse
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p = argparse.ArgumentParser(description="FTDT Quant Lab CLI")
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@@ -578,6 +624,13 @@ def main():
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pd.add_argument("--end-date", default="2026-08-07")
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pd.add_argument("--horizons", default="100,500,1000,5000,10000", help="Comma-separated ms horizons")
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# funding
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pf = sp.add_parser("funding", help="Funding rate arb discovery — analyze historical funding rates")
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pf.add_argument("--data-dir", default="data/raw")
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pf.add_argument("--coin", default="BTC")
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pf.add_argument("--start-date", default="2026-01-01")
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pf.add_argument("--end-date", default="2030-01-01")
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args = p.parse_args()
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import json as _json
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@@ -598,6 +651,8 @@ def main():
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cmd_tick_backtest(args)
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elif args.command == "discover":
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cmd_discover(args)
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elif args.command == "funding":
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cmd_funding(args)
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if __name__ == "__main__":
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+121
-14
@@ -27,13 +27,16 @@ from typing import Optional
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from live.treasury import Treasury
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from sim.maker import AvellanedaStoikovMaker, MakerConfig, Quote
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from sim.queue import QueueModel
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from live.filters.toxicity import ToxicityFilter
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from live.makers.hl_btc_eth import HlMakerPool
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from live.treasury import Treasury
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from live.integrator import AnalyticsPipeline
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from live.monitors.cross_venue import CrossVenueMonitor
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from live.monitors.funding_basis import FundingBasisMonitor
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from live.monitors.liq_risk import LiquidationRiskOverlay
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from sim.fills import QueueAwareFillModel
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from live.makers.hl_btc_eth import HlMakerPool
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logger = logging.getLogger("ftdt-node-v2")
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@@ -103,6 +106,25 @@ class ProductionNode:
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for coin in coins:
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self._maker_pool.add_maker(coin.upper(), max_inventory=max_position_per_coin)
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# Queue-aware fill model for realistic paper trading
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self._fill_model = QueueAwareFillModel()
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# WQI predictor — directional strategy from queue imbalance
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from strategies.wqi_predictor import WQIPredictor
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self._wqi_predictors = {
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coin: WQIPredictor(z_entry=2.0, max_hold_seconds=30,
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stop_loss_bps=5.0, take_profit_bps=10.0,
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size=base_quote_size, fee_model="taker")
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for coin in coins
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}
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# Funding arb strategy
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from strategies.funding_arb_strategy import FundingArb
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self._funding_arb = FundingArb(
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apr_threshold=0.30, apr_exit=0.10, size=base_quote_size * 5,
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max_hold_hours=48.0, taker_fee_pct=0.00045,
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)
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# Monitors
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self._cross_venue = CrossVenueMonitor()
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self._funding_monitor = FundingBasisMonitor()
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@@ -184,15 +206,17 @@ class ProductionNode:
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# 6. Generate quotes
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quotes = self._maker_pool.quote_all()
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# 7. Simulate fills (paper mode — mark-based)
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# 7. Simulate fills (paper mode — queue-aware)
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if self._mode == "paper":
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for coin in self._coins:
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q = quotes.get(coin)
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pipeline = self._pipelines[coin]
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if q:
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pipeline = self._pipelines[coin]
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self._simulate_paper_fills(coin, q, pipeline)
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if self._tick % 3 == 0:
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self._simulate_wqi_trades(coin, pipeline)
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# 8. Update funding monitor
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# 8. Update funding monitor and check funding arb
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for coin in self._coins:
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funding = await self._fetch_funding(coin)
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if funding is not None:
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@@ -270,19 +294,30 @@ class ProductionNode:
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# ── Paper trading ────────────────────────────────────────
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def _simulate_paper_fills(self, coin: str, quote, pipeline: AnalyticsPipeline):
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"""Naive paper fill: if our bid > mid or ask < mid after some random threshold,
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simulate a fill. In production this comes from exchange WebSocket."""
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import random
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mid = pipeline.mid
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if mid <= 0:
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if mid <= 0 or quote is None:
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return
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if random.random() < 0.05:
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side = "bid" if random.random() < 0.5 else "ask"
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size = getattr(quote, f"{side}_size", 0.001)
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px = getattr(quote, side, mid)
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bid_fill = self._fill_model.check_fill(
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aggressor_side="sell",
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agg_size=pipeline._depth_ask or 0.1,
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agg_price=max(getattr(quote, "bid", mid) - 1, 1),
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our_price=getattr(quote, "bid", mid),
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our_size=getattr(quote, "bid_size", 0.0002),
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depth_ahead=self._fill_model.estimate_depth_ahead(
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our_price=getattr(quote, "bid", mid),
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our_side="bid",
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best_bid=pipeline._best_bid,
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best_ask=pipeline._best_ask,
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bid_depth=pipeline._depth_bid or 1.0,
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ask_depth=pipeline._depth_ask or 1.0,
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),
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)
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if bid_fill["filled"]:
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side = "buy"
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size = bid_fill["fill_size"]
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px = getattr(quote, "bid", mid)
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fee = size * px * 0.0002
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can = self._treasury.can_open(coin, side, size, px)
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if can["allowed"]:
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self._treasury.record_fill(coin, side, size, px, fee, pnl=0)
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@@ -290,6 +325,71 @@ class ProductionNode:
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if maker:
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maker.record_fill(side, size, px, fee)
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ask_fill = self._fill_model.check_fill(
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aggressor_side="buy",
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agg_size=pipeline._depth_bid or 0.1,
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agg_price=min(getattr(quote, "ask", mid) + 1, mid * 2),
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our_price=getattr(quote, "ask", mid),
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our_size=getattr(quote, "ask_size", 0.0002),
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depth_ahead=self._fill_model.estimate_depth_ahead(
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our_price=getattr(quote, "ask", mid),
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our_side="ask",
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best_bid=pipeline._best_bid,
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best_ask=pipeline._best_ask,
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bid_depth=pipeline._depth_bid or 1.0,
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ask_depth=pipeline._depth_ask or 1.0,
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),
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)
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if ask_fill["filled"]:
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side = "sell"
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size = ask_fill["fill_size"]
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px = getattr(quote, "ask", mid)
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fee = size * px * 0.0002
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can = self._treasury.can_open(coin, side, size, px)
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if can["allowed"]:
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self._treasury.record_fill(coin, side, size, px, fee, pnl=0)
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maker = self._maker_pool.get(coin)
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if maker:
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maker.record_fill(side, size, px, fee)
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def _simulate_wqi_trades(self, coin: str, pipeline: AnalyticsPipeline):
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mid = pipeline.mid
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if mid <= 0:
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return
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predictor = self._wqi_predictors.get(coin)
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if predictor is None:
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return
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bids_list = [(pipeline._best_bid, pipeline._depth_bid or 1.0)]
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asks_list = [(pipeline._best_ask, pipeline._depth_ask or 1.0)]
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signal = predictor.feed_signal(bids_list, asks_list, mid)
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if signal["action"] in ("BUY", "SELL"):
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side = signal["action"].lower()
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size = 0.0002
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px = mid
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fee = size * px * 0.0005
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can = self._treasury.can_open(coin, side, size, px)
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if can["allowed"]:
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self._treasury.record_fill(coin, side, size, px, fee, pnl=0)
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fee_paid = size * px * 0.0005
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self._treasury._fees_paid += fee_paid
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logger.info(f"[WQI-{coin}] {signal['action']} signal: "
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f"z={signal['z_score']:.2f} wqi={signal['wqi']:.3f} "
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f"reason={signal['reason']}")
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elif signal["action"] == "EXIT":
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pos = self._treasury.position(coin)
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if abs(pos) > 0:
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side = "sell" if pos > 0 else "buy"
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fee = abs(pos) * mid * 0.0005
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pnl = pos * (mid - predictor._entry_price) if predictor._entry_price > 0 else 0
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self._treasury.record_fill(coin, side, abs(pos), mid, fee, pnl)
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logger.info(f"[WQI-{coin}] EXIT: z={signal['z_score']:.2f} "
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f"pnl=${pnl:.4f} reason={signal['reason']}")
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# ── Dashboard ────────────────────────────────────────────
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def _write_metrics(self):
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@@ -305,6 +405,13 @@ class ProductionNode:
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"treasury": self._treasury.summary(),
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"analytics": {c: p.emit() for c, p in self._pipelines.items()},
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"maker": self._maker_pool.summary(),
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"wqi": {c: p.summary() for c, p in self._wqi_predictors.items()},
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"funding_arb": self._funding_arb.summary(),
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"fill_model": {
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"fill_rate": round(self._fill_model.fill_rate(), 4),
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"fills": self._fill_model.fill_count,
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"skips": self._fill_model.skip_count,
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},
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"funding": self._funding_monitor.summary(),
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"cross_venue": self._cross_venue.summary("BTC"),
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"equity_history": self._equity_history,
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@@ -0,0 +1,353 @@
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"""
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Funding Rate Arbitrage — backtestable strategy module.
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Delta-neutral carry trade on Hyperliquid perps. When funding rate is high:
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- Short the perpetual (collect funding payments)
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- The profit is the funding rate, not price direction
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Features:
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- Configurable entry/exit thresholds
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- Position sizing proportional to funding rate
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- Funding payment tracking with accurate Hyperliquid 8h schedule
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- Max hold time (exit after N hours regardless)
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- Stop-loss if basis widens (mark price moves against funding direction)
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- Per-trade PnL accounting with fees, funding, and mark PnL
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Usage (backtest):
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arb = FundingArb(apr_threshold=0.30, apr_exit=0.10, size=0.001)
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for hourly_funding in funding_history:
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trade = arb.tick(funding_rate, mark_price, timestamp)
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if trade:
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print(f"Trade: {trade}")
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Usage (live):
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arb = FundingArb(apr_threshold=0.30)
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signal = arb.signal(check_rates(time.time()))
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if signal["action"] != "HOLD":
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execute(signal)
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"""
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from __future__ import annotations
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import time
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from collections import deque
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from typing import Optional
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class FundingArb:
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"""Delta-neutral funding rate carry strategy.
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Logic:
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- Entry: |annualized_funding| > apr_threshold AND no position
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- Exit: |annualized_funding| < apr_exit
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OR hold_time > max_hold_hours
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OR funding direction flips (paying instead of collecting)
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OR basis stop-loss triggered
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"""
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def __init__(
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self,
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apr_threshold: float = 0.30,
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apr_exit: float = 0.10,
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size: float = 0.001,
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max_hold_hours: float = 48.0,
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basis_stop_loss_pct: float = 0.03,
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taker_fee_pct: float = 0.00045,
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maker_fee_pct: float = 0.00015,
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):
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self._apr_threshold = apr_threshold
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self._apr_exit = apr_exit
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self._size = size
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self._max_hold_seconds = max_hold_hours * 3600
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self._basis_stop_loss = basis_stop_loss_pct
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self._taker_fee = taker_fee_pct
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self._maker_fee = maker_fee_pct
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self._position: int = 0
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self._entry_price: float = 0.0
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self._entry_time: float = 0.0
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self._entry_apr: float = 0.0
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self._funding_collected: float = 0.0
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self._funding_paid: float = 0.0
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self._trades: list[dict] = []
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self._funding_history: deque[float] = deque(maxlen=200)
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self._mark_history: deque[float] = deque(maxlen=200)
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self._signals: deque[dict] = deque(maxlen=50)
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@property
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def position(self) -> int:
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return self._position
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@property
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def trades(self) -> list[dict]:
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return self._trades
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def tick(
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self,
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funding_rate_annual: float,
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mark_price: float,
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timestamp: Optional[float] = None,
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) -> dict | None:
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"""Process one funding rate observation. Returns trade dict if entry/exit occurred."""
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if timestamp is None:
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timestamp = time.time()
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if mark_price <= 0:
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return None
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self._funding_history.append(funding_rate_annual)
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self._mark_history.append(mark_price)
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abs_apr = abs(funding_rate_annual)
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action = "HOLD"
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trade = None
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if self._position == 0:
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if abs_apr > self._apr_threshold:
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action = "SELL" if funding_rate_annual > 0 else "BUY"
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self._position = -1 if funding_rate_annual > 0 else 1
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self._entry_price = mark_price
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self._entry_time = timestamp
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self._entry_apr = funding_rate_annual
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notional = self._size * mark_price
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fee = notional * self._taker_fee
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self._funding_paid += fee
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trade = {
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"action": action,
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"side": action,
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"size": self._size,
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"entry_price": mark_price,
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"apr": round(funding_rate_annual, 4),
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"apr_pct": round(funding_rate_annual * 100, 2),
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"fee": round(fee, 4),
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}
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self._signals.append({
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"timestamp": timestamp,
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"action": action,
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"apr": funding_rate_annual,
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"price": mark_price,
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})
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else:
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hold_seconds = timestamp - self._entry_time
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direction = "short" if self._position == -1 else "long"
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exit_reason = ""
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if abs_apr < self._apr_exit:
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exit_reason = f"apr_faded_to_{abs_apr*100:.1f}%"
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elif hold_seconds >= self._max_hold_seconds:
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exit_reason = f"max_hold_{hold_seconds/3600:.1f}h"
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elif (self._position == -1 and funding_rate_annual < 0) or \
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(self._position == 1 and funding_rate_annual > 0):
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exit_reason = f"funding_flipped_to_{funding_rate_annual*100:.2f}%"
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else:
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price_move = (mark_price - self._entry_price) / self._entry_price
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position_pnl_pct = price_move * self._position
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if abs(position_pnl_pct) > self._basis_stop_loss:
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exit_reason = f"basis_stop_loss_{position_pnl_pct*100:.2f}%"
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|
||||
if exit_reason:
|
||||
notional = self._size * mark_price
|
||||
fee = notional * self._taker_fee
|
||||
|
||||
price_pnl = self._size * (mark_price - self._entry_price) * self._position
|
||||
funding_earned = 0.0
|
||||
if isinstance(self._entry_apr, float) and self._entry_apr != 0:
|
||||
funding_rate_8h = self._entry_apr / 1095
|
||||
funding_intervals = hold_seconds / (8 * 3600)
|
||||
funding_earned = notional * abs(funding_rate_8h) * funding_intervals * 0.95
|
||||
|
||||
net_pnl = price_pnl + funding_earned - fee - self._funding_paid
|
||||
|
||||
action = "BUY" if self._position == -1 else "SELL"
|
||||
|
||||
trade = {
|
||||
"action": f"EXIT_{exit_reason}",
|
||||
"side": action,
|
||||
"entry_price": round(self._entry_price, 2),
|
||||
"exit_price": round(mark_price, 2),
|
||||
"size": self._size,
|
||||
"direction": direction,
|
||||
"hold_hours": round(hold_seconds / 3600, 2),
|
||||
"entry_apr": round(self._entry_apr * 100, 2),
|
||||
"exit_apr": round(funding_rate_annual * 100, 2),
|
||||
"price_pnl": round(price_pnl, 4),
|
||||
"funding_earned": round(funding_earned, 4),
|
||||
"fees": round(fee + self._funding_paid, 4),
|
||||
"net_pnl": round(net_pnl, 4),
|
||||
"reason": exit_reason,
|
||||
}
|
||||
self._trades.append(trade)
|
||||
self._signals.append({
|
||||
"timestamp": timestamp,
|
||||
"action": "EXIT",
|
||||
"apr": funding_rate_annual,
|
||||
"price": mark_price,
|
||||
"reason": exit_reason,
|
||||
"pnl": net_pnl,
|
||||
})
|
||||
|
||||
self._position = 0
|
||||
self._entry_price = 0.0
|
||||
self._entry_time = 0.0
|
||||
self._entry_apr = 0.0
|
||||
self._funding_paid = 0.0
|
||||
|
||||
return trade
|
||||
|
||||
def signal(
|
||||
self,
|
||||
funding_rate_annual: float,
|
||||
mark_price: float = 0.0,
|
||||
timestamp: Optional[float] = None,
|
||||
) -> dict:
|
||||
"""Generate trading signal without executing."""
|
||||
if timestamp is None:
|
||||
timestamp = time.time()
|
||||
|
||||
abs_apr = abs(funding_rate_annual)
|
||||
|
||||
if self._position == 0 and abs_apr > self._apr_threshold:
|
||||
return {
|
||||
"action": "SELL" if funding_rate_annual > 0 else "BUY",
|
||||
"size": self._size,
|
||||
"apr": round(funding_rate_annual * 100, 2),
|
||||
"reason": f"apr_{abs_apr*100:.1f}%_above_{self._apr_threshold*100:.0f}%",
|
||||
}
|
||||
elif self._position != 0 and abs_apr < self._apr_exit:
|
||||
return {
|
||||
"action": "EXIT",
|
||||
"reason": f"apr_faded_to_{abs_apr*100:.1f}%",
|
||||
}
|
||||
|
||||
return {"action": "HOLD"}
|
||||
|
||||
def summary(self) -> dict:
|
||||
if not self._trades:
|
||||
return {
|
||||
"total_trades": 0,
|
||||
"win_rate": 0.0,
|
||||
"total_net_pnl": 0.0,
|
||||
"avg_net_pnl": 0.0,
|
||||
"avg_hold_hours": 0.0,
|
||||
"total_funding_earned": 0.0,
|
||||
"position": self._position,
|
||||
}
|
||||
|
||||
wins = sum(1 for t in self._trades if t["net_pnl"] > 0)
|
||||
total_net = sum(t["net_pnl"] for t in self._trades)
|
||||
total_funding = sum(t["funding_earned"] for t in self._trades)
|
||||
hold_hours = [t["hold_hours"] for t in self._trades]
|
||||
|
||||
return {
|
||||
"total_trades": len(self._trades),
|
||||
"win_rate": round(wins / len(self._trades), 3),
|
||||
"total_net_pnl": round(total_net, 4),
|
||||
"avg_net_pnl": round(total_net / len(self._trades), 4),
|
||||
"avg_hold_hours": round(sum(hold_hours) / len(hold_hours), 2),
|
||||
"total_funding_earned": round(total_funding, 4),
|
||||
"best_trade": round(max(t["net_pnl"] for t in self._trades), 4),
|
||||
"worst_trade": round(min(t["net_pnl"] for t in self._trades), 4),
|
||||
"position": self._position,
|
||||
}
|
||||
|
||||
def reset(self):
|
||||
self._position = 0
|
||||
self._entry_price = 0.0
|
||||
self._entry_time = 0.0
|
||||
self._entry_apr = 0.0
|
||||
self._funding_collected = 0.0
|
||||
self._funding_paid = 0.0
|
||||
self._trades.clear()
|
||||
self._signals.clear()
|
||||
self._funding_history.clear()
|
||||
self._mark_history.clear()
|
||||
|
||||
|
||||
def backtest_funding_arb(
|
||||
funding_rates: list[float],
|
||||
mark_prices: list[float],
|
||||
apr_threshold: float = 0.30,
|
||||
size: float = 0.001,
|
||||
taker_fee_pct: float = 0.00045,
|
||||
) -> dict:
|
||||
"""Run funding arb backtest on a series of funding rate observations.
|
||||
|
||||
Args:
|
||||
funding_rates: list of annualized funding rates (e.g., from HL API)
|
||||
mark_prices: list of corresponding mark prices
|
||||
apr_threshold: minimum annual APR to enter
|
||||
size: trade size
|
||||
taker_fee_pct: taker fee per trade
|
||||
|
||||
Returns dict with trades and summary.
|
||||
"""
|
||||
arb = FundingArb(
|
||||
apr_threshold=apr_threshold,
|
||||
size=size,
|
||||
taker_fee_pct=taker_fee_pct,
|
||||
)
|
||||
|
||||
min_len = min(len(funding_rates), len(mark_prices))
|
||||
for i in range(min_len):
|
||||
arb.tick(funding_rates[i], mark_prices[i], float(i))
|
||||
|
||||
return arb.summary()
|
||||
|
||||
|
||||
def run_funding_discovery(data_dir: str = "data/raw", coin: str = "BTC",
|
||||
start_date: str = "2026-01-01", end_date: str = "2030-01-01") -> dict:
|
||||
"""Run funding rate discovery — analyze historical funding rates to find
|
||||
optimal entry/exit thresholds.
|
||||
|
||||
Returns dict with rate distribution percentiles and backtest results at different thresholds.
|
||||
"""
|
||||
import numpy as np
|
||||
from data.store import read_range
|
||||
|
||||
msgs = read_range(data_dir, channel="funding", coin=coin,
|
||||
start_date=start_date, end_date=end_date)
|
||||
if not msgs:
|
||||
return {"error": "No funding data available"}
|
||||
|
||||
rates = []
|
||||
marks = []
|
||||
for msg in msgs:
|
||||
payload = msg.get("payload", {})
|
||||
rate = float(payload.get("funding", 0))
|
||||
mark = float(payload.get("mark_px", 0))
|
||||
if mark > 0:
|
||||
annual = rate * 1095
|
||||
rates.append(annual)
|
||||
marks.append(mark)
|
||||
|
||||
if not rates:
|
||||
return {"error": "No valid funding observations"}
|
||||
|
||||
a = np.array(rates)
|
||||
abs_a = np.abs(a)
|
||||
percentiles = [10, 25, 50, 75, 90, 95, 99]
|
||||
|
||||
result = {
|
||||
"n_observations": len(rates),
|
||||
"rate_distribution": {
|
||||
"mean_apr_pct": round(float(np.mean(a)) * 100, 2),
|
||||
"std_apr_pct": round(float(np.std(a)) * 100, 2),
|
||||
"max_apr_pct": round(float(np.max(a)) * 100, 2),
|
||||
"min_apr_pct": round(float(np.min(a)) * 100, 2),
|
||||
"abs_percentiles": {
|
||||
f"p{p}": round(float(np.percentile(abs_a, p)) * 100, 2)
|
||||
for p in percentiles
|
||||
},
|
||||
},
|
||||
"backtests": {},
|
||||
}
|
||||
|
||||
for threshold in [0.05, 0.10, 0.20, 0.30, 0.50]:
|
||||
summary = backtest_funding_arb(rates, marks, apr_threshold=threshold)
|
||||
if summary["total_trades"] > 0:
|
||||
result["backtests"][f"apr_{int(threshold*100)}pct"] = summary
|
||||
|
||||
return result
|
||||
@@ -0,0 +1,205 @@
|
||||
"""
|
||||
Tests for funding arb strategy and WQI predictor integration.
|
||||
"""
|
||||
import math
|
||||
|
||||
|
||||
class TestFundingArb:
|
||||
def test_no_entry_below_threshold(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30)
|
||||
trade = arb.tick(0.10, 100000.0, 0.0)
|
||||
assert trade is None
|
||||
assert arb.position == 0
|
||||
|
||||
def test_entry_above_threshold_positive(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30)
|
||||
trade = arb.tick(0.50, 100000.0, 0.0)
|
||||
assert trade is not None
|
||||
assert trade["action"] == "SELL"
|
||||
assert arb.position == -1
|
||||
|
||||
def test_entry_above_threshold_negative(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30)
|
||||
trade = arb.tick(-0.50, 100000.0, 0.0)
|
||||
assert trade is not None
|
||||
assert trade["action"] == "BUY"
|
||||
assert arb.position == 1
|
||||
|
||||
def test_exit_when_apr_fades(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30, apr_exit=0.10)
|
||||
arb.tick(0.50, 100000.0, 0.0)
|
||||
trade = arb.tick(0.05, 100000.0, 3600.0)
|
||||
assert trade is not None
|
||||
assert "EXIT" in trade["action"]
|
||||
assert arb.position == 0
|
||||
|
||||
def test_exit_when_funding_flips(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30, apr_exit=0.10)
|
||||
arb.tick(0.50, 100000.0, 0.0)
|
||||
trade = arb.tick(-0.10, 100000.0, 3600.0)
|
||||
assert trade is not None
|
||||
assert "EXIT" in trade["action"]
|
||||
assert arb.position == 0
|
||||
|
||||
def test_signal_no_exit_when_apr_still_high(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30)
|
||||
arb.tick(0.50, 100000.0, 0.0)
|
||||
result = arb.signal(0.60, 100000.0)
|
||||
assert result["action"] == "HOLD"
|
||||
|
||||
def test_signal_hold_when_below_threshold(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30)
|
||||
result = arb.signal(0.05)
|
||||
assert result["action"] == "HOLD"
|
||||
|
||||
def test_summary_no_trades(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb()
|
||||
s = arb.summary()
|
||||
assert s["total_trades"] == 0
|
||||
assert s["win_rate"] == 0.0
|
||||
|
||||
def test_summary_with_trades(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30, apr_exit=0.10)
|
||||
arb.tick(0.50, 100000.0, 0.0)
|
||||
arb.tick(0.05, 100100.0, 3600.0)
|
||||
arb.tick(0.50, 100000.0, 7200.0)
|
||||
arb.tick(0.05, 100050.0, 10800.0)
|
||||
s = arb.summary()
|
||||
assert s["total_trades"] == 2
|
||||
assert s["position"] == 0
|
||||
|
||||
def test_reset(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.30, apr_exit=0.10)
|
||||
arb.tick(0.50, 100000.0, 0.0)
|
||||
arb.reset()
|
||||
assert arb.position == 0
|
||||
assert len(arb.trades) == 0
|
||||
|
||||
def test_backtest_empty(self):
|
||||
from strategies.funding_arb_strategy import backtest_funding_arb
|
||||
result = backtest_funding_arb([], [])
|
||||
assert result["total_trades"] == 0
|
||||
|
||||
def test_backtest_single_trade(self):
|
||||
from strategies.funding_arb_strategy import backtest_funding_arb
|
||||
rates = [0.50, 0.06]
|
||||
prices = [100000.0, 100000.0]
|
||||
result = backtest_funding_arb(rates, prices, apr_threshold=0.30)
|
||||
assert result["total_trades"] == 1
|
||||
|
||||
def test_fee_accounting(self):
|
||||
from strategies.funding_arb_strategy import FundingArb
|
||||
arb = FundingArb(apr_threshold=0.10, size=0.001, taker_fee_pct=0.00045)
|
||||
trade = arb.tick(0.50, 100000.0, 0.0)
|
||||
assert trade is not None
|
||||
expected_fee = 0.001 * 100000.0 * 0.00045
|
||||
assert abs(trade["fee"] - expected_fee) < 0.001
|
||||
|
||||
|
||||
class TestFundingDiscoveryCLI:
|
||||
def test_discovery_no_data(self):
|
||||
from strategies.funding_arb_strategy import run_funding_discovery
|
||||
result = run_funding_discovery(
|
||||
data_dir="/tmp/nonexistent_data",
|
||||
coin="BTC",
|
||||
)
|
||||
assert "error" in result
|
||||
|
||||
def test_backtest_multiple_thresholds(self):
|
||||
from strategies.funding_arb_strategy import backtest_funding_arb
|
||||
import random
|
||||
random.seed(42)
|
||||
rates = [abs(random.gauss(0, 0.5)) for _ in range(200)]
|
||||
prices = [100000.0 + random.gauss(0, 500) for _ in range(200)]
|
||||
|
||||
for threshold in [0.10, 0.30, 0.50]:
|
||||
result = backtest_funding_arb(rates, prices, apr_threshold=threshold)
|
||||
assert "total_trades" in result
|
||||
assert "total_net_pnl" in result
|
||||
|
||||
|
||||
class TestWQIIntegration:
|
||||
def test_wqi_with_node_interface(self):
|
||||
from strategies.wqi_predictor import WQIPredictor
|
||||
wqi = WQIPredictor(z_entry=2.0, max_hold_seconds=30)
|
||||
bids = [(50000.0, 1.0), (49999.0, 0.5)]
|
||||
asks = [(50002.0, 1.0), (50003.0, 0.5)]
|
||||
|
||||
for _ in range(30):
|
||||
wqi.feed_signal(bids, asks, 50001.0)
|
||||
|
||||
extreme_bids = [(50000.0, 10.0), (49999.0, 5.0)]
|
||||
extreme_asks = [(50002.0, 0.5)]
|
||||
signal = wqi.feed_signal(extreme_bids, extreme_asks, 50001.0)
|
||||
|
||||
assert signal["action"] in ("BUY", "HOLD")
|
||||
if signal["action"] == "BUY":
|
||||
assert wqi.position != 0
|
||||
|
||||
def test_wqi_exit_on_timeout(self):
|
||||
from strategies.wqi_predictor import WQIPredictor
|
||||
import time
|
||||
wqi = WQIPredictor(z_entry=0.01, z_exit=999.0, wqi_threshold=0.01,
|
||||
max_hold_seconds=0.001, max_adverse=999.0)
|
||||
|
||||
for _ in range(30):
|
||||
wqi.feed_signal([(100.0, 1.0)], [(102.0, 1.0)], 101.0)
|
||||
|
||||
extreme_bids = [(100.0, 20.0), (99.0, 10.0)]
|
||||
extreme_asks = [(102.0, 1.0)]
|
||||
signal = wqi.feed_signal(extreme_bids, extreme_asks, 101.0)
|
||||
|
||||
if signal["action"] in ("BUY", "SELL"):
|
||||
time.sleep(0.01)
|
||||
signal2 = wqi.feed_signal(extreme_bids, extreme_asks, 101.0)
|
||||
assert signal2["action"] in ("EXIT", "HOLD")
|
||||
|
||||
|
||||
class TestNodeV2Strategies:
|
||||
def test_node_creates_all_strategies(self):
|
||||
from live.node_v2 import ProductionNode
|
||||
node = ProductionNode(
|
||||
coins=["BTC"],
|
||||
testnet=True,
|
||||
mode="paper",
|
||||
max_position_per_coin=0.001,
|
||||
base_quote_size=0.0001,
|
||||
)
|
||||
assert len(node._wqi_predictors) == 1
|
||||
assert node._funding_arb is not None
|
||||
assert node._fill_model is not None
|
||||
|
||||
def test_wqi_not_none(self):
|
||||
from live.node_v2 import ProductionNode
|
||||
node = ProductionNode(
|
||||
coins=["BTC"],
|
||||
testnet=True,
|
||||
mode="paper",
|
||||
max_position_per_coin=0.001,
|
||||
)
|
||||
wqi = node._wqi_predictors.get("BTC")
|
||||
assert wqi is not None
|
||||
assert wqi._z_entry == 2.0
|
||||
assert wqi._max_hold_seconds == 30
|
||||
|
||||
def test_funding_arb_config(self):
|
||||
from live.node_v2 import ProductionNode
|
||||
node = ProductionNode(
|
||||
coins=["BTC"],
|
||||
testnet=True,
|
||||
mode="paper",
|
||||
max_position_per_coin=0.001,
|
||||
)
|
||||
arb = node._funding_arb
|
||||
assert arb._apr_threshold == 0.30
|
||||
assert arb._apr_exit == 0.10
|
||||
Reference in New Issue
Block a user