fly-trader distribution: code, fly #62, selector #59, connectome, seed (branch distribution @ 5cb1652)
0f038be verified | """Decision points: labels, pessimistic fills, the past-only scale-break rule, holds and per-day accounting.""" | |
| from datetime import datetime, timedelta, timezone | |
| import numpy as np | |
| import pandas as pd | |
| import pyarrow as pa | |
| import pyarrow.parquet as pq | |
| import pytest | |
| from fly_trader.market.features import FEATURES | |
| from fly_trader.train import decisions | |
| from fly_trader.train.mature import SCHEMA | |
| def _part(tmp_path, rows, version=None): | |
| from fly_trader.market.features import FEATURE_VERSION | |
| from fly_trader.train.mature import AGG_VERSION, write_part | |
| write_part(pa.Table.from_pylist(rows, schema=SCHEMA), tmp_path / "2026-09-01" / "part.parquet", FEATURE_VERSION if version is None else version, {"fly_agg": AGG_VERSION}) | |
| return tmp_path | |
| def _row(mint, ts, close, resq=100.0, open_=None, logvol_15m=3.0): | |
| r = {"mint": mint, "ts": ts, "phase": "amm", "t_rel_min": 10, "open": open_ if open_ is not None else close, "high": close, "low": close, "close": close, | |
| "volume_sol": 10.0, "resq": resq, "age_h": 2.0, "has_trades": False, "mask": 0, | |
| "pre_minutes": 0.0, "pre_trades": 0.0, "pre_buyers": 0.0, "pre_vol_sol": 0.0, "pre_top10_pct": 0.0, | |
| "traders_15m": 5.0, "traders_1h": 10.0, "n_trades_1m": 2.0} | |
| for f in FEATURES: | |
| r[f] = 0.0 | |
| r["logvol_15m"] = logvol_15m | |
| return r | |
| def test_labels_fills_and_past_only_rule(tmp_path): | |
| t0 = datetime(2026, 9, 1, 12, 0, tzinfo=timezone.utc) | |
| rows = [] | |
| # token A: flat 1.0 for 40 minutes, then +10 % at minute 20 relative to a decision at minute 0 | |
| for k in range(80): | |
| px = 1.0 if k < 20 else 1.10 | |
| rows.append(_row("A", t0 + timedelta(minutes=k), px, open_=px * 1.01 if k == 1 else px)) | |
| # token B: normal for 30 minutes, then a 100x print at minute 30 (scale break), then normal again | |
| for k in range(80): | |
| px = 1.0 if k != 30 else 100.0 | |
| rows.append(_row("B", t0 + timedelta(minutes=k), px)) | |
| ds = decisions.build(days=None, feature_dir=_part(tmp_path, rows), horizon_min=30, fee=0.0) | |
| a = ds.mint == "A"; b = ds.mint == "B" | |
| # A at minute 0 sees the close at minute 30 (1.10): +10 %; pessimistic fill uses minute 1's open (1.01) | |
| i0 = np.flatnonzero(a & (ds.ts == t0.timestamp()))[0] | |
| assert ds.fwd[i0] == pytest.approx(0.10, abs=1e-6) | |
| assert ds.fwd_pess[i0] == pytest.approx(1.10 / 1.01 - 1, abs=1e-6) | |
| assert ds.y[i0] == 1 | |
| # the last 35 minutes of the file have no full horizon: ineligible | |
| assert ds.ts.max() <= (t0 + timedelta(minutes=80 - 36)).timestamp() | |
| # B: rows before the break are eligible and their labels see the 100x print (no leak in either direction); | |
| # rows from the break onward are gone | |
| tb = ds.ts[b] | |
| assert tb.max() < (t0 + timedelta(minutes=30)).timestamp() | |
| assert not (b & (ds.ts == t0.timestamp())).any() # its 30-minute exit lands on the reverting 100x print: not a label | |
| ib5 = np.flatnonzero(b & (ds.ts == (t0 + timedelta(minutes=5)).timestamp()))[0] | |
| assert ds.fwd[ib5] == pytest.approx(0.0, abs=1e-6) # its mark (minute 35) is back at 1.0 | |
| def test_model_costs_on_both_sides_and_random_baseline(tmp_path): | |
| t0 = datetime(2026, 9, 1, 12, 0, tzinfo=timezone.utc) | |
| rows = [] | |
| for k in range(80): # flat price; entry minutes cost 1 %, the rest 2 % one-way | |
| r = _row("C", t0 + timedelta(minutes=k), 1.0); r["exit_cost_0p1"] = 0.01 if k < 10 else 0.02; rows.append(r) | |
| ds = decisions.build(days=None, feature_dir=_part(tmp_path, rows), horizon_min=30) # default: the paper broker's cost model | |
| i0 = np.flatnonzero(ds.ts == t0.timestamp())[0] | |
| from fly_trader.market.exit_cost import cost_at_size # costs are charged at the size the book trades | |
| e_in, e_out = cost_at_size(0.01, 100.0), cost_at_size(0.02, 100.0) | |
| assert e_in > 0.01 and e_out > 0.02 # dearer than the stored 0.1 SOL figure | |
| assert ds.fwd[i0] == pytest.approx((1 - e_in) * (1 - e_out) - 1, abs=1e-6) # entry cost at the signal minute, exit cost at the exit minute | |
| rr = decisions.random_trades(ds, np.ones(len(ds.y), bool), 3, seeds=2) | |
| assert len(rr) >= 2 and (rr < 0).all() # a flat market loses exactly the costs | |
| assert len(decisions.random_trades(ds, np.ones(len(ds.y), bool), 0)) == 0 | |
| def test_trades_respect_hold_and_summaries(): | |
| ts = np.array([0.0, 60.0, 120.0, 1800.0, 1900.0, 0.0], dtype=float) | |
| ds = decisions.DecisionSet(X=np.zeros((6, 1), np.float32), y=np.zeros(6, np.int8), fwd=np.array([0.1, 0.2, 0.3, 0.4, 0.5, -0.1], np.float32), | |
| fwd_pess=np.array([0.1, 0.2, 0.3, 0.4, 0.5, -0.1], np.float32), | |
| day=np.array([datetime(2026, 9, 1).date()] * 5 + [datetime(2026, 9, 2).date()], dtype=object), | |
| ts=ts, mint=np.array(["A", "A", "A", "A", "A", "B"]), cols=["x"], horizon_s=1800.0) | |
| r = decisions.trades_from_picks(ds, np.ones(6, bool)) | |
| assert list(r) == pytest.approx([0.1, 0.4, -0.1]) # A re-enters only after the 30-minute hold; B once | |
| s = decisions.summarize(np.array([0.1, 0.2], np.float32)) | |
| assert s["pf"] is None and s["win"] == 1.0 # no losing trade → profit factor undefined, never inf | |
| ev = decisions.evaluate(ds, np.ones(6), np.ones(6, bool), 0.5) | |
| assert ev["pooled"]["n"] == 3 and ev["per_day"]["2026-09-01"]["n"] == 2 and ev["per_day"]["2026-09-02"]["n"] == 1 | |
| def test_stale_feature_parts_are_refused(tmp_path): | |
| t0 = datetime(2026, 9, 1, 12, 0, tzinfo=timezone.utc) | |
| rows = [_row("A", t0 + timedelta(minutes=k), 1.0) for k in range(80)] | |
| with pytest.raises(RuntimeError, match="feature or aggregation version"): | |
| decisions.build(days=None, feature_dir=_part(tmp_path, rows, version=1), horizon_min=30, fee=0.0) | |