forked from mnbvc188199/Warrior_EA
626b591ca6
Owner decision (stated twice): available data gets wired; the networks judge
usefulness; the deploy gate remains the arbiter of what trades. Implemented:
MACRO block (6) on every symbol: 10y yield 20d change, curve slope, 5y
breakeven 20d change, Fed-ECB policy gap, CPI yoy, unemployment 12m change.
Screened null vs forward range on all four research symbols - recorded as
the honest prior in the catalog comment, wired regardless.
RISK block (3) extended to every symbol (FX majors, metals, energy, BTC all
now carry vix/vix_chg5/usd_chg5).
IVOL pair extended with the level alongside the change.
Vintage integrity kept where it is free: CPI is fetched as CPIAUCNS (NSA,
essentially never revised) so the plain-FRED backfill stays first-print-clean;
yields/curve/breakevens/policy rates are unrevised by nature. UNRATE is the
one exception (seasonal refits, ~0.1-0.2pp) - the EA cannot run the ALFRED
protocol, accepted and documented at the declaration site.
UpdateFred gains a staleDays parameter so the monthly series do not fire a
pointless fetch attempt every hour for three weeks after each print.
FeatureValue now takes the day and does its own as-of lookups - adding a
source no longer widens a parameter list. Feature counts: 12-15 per symbol;
symbol feature-order changed, safe only because no models exist yet.
export.py mirrors the new catalog for the five research symbols (13-15
features), smoke-tested: all five CSVs written, 6,072 daily rows each.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
204 lines
10 KiB
Python
204 lines
10 KiB
Python
"""Export EA-facing alt-data feature files.
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Writes Common\\Files\\Warrior_EA\\AltData\\{SYM}_D1.csv with the features that
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survived BOTH the marginal family-wise bar and the incremental
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(conditional-on-trailing-range) test - see DESIGN.md and screen.py results
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(commits 4a56d8d, 6d50e63) - PLUS the exploratory EIA petroleum block on every
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symbol (user directive 2026-08-16: "the NN might find patterns in it for both
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oil and regular symbols"; screened null on WTI's short sample, so it ships as
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exploratory input, not certified edge). For the symbols listed here the columns
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must stay identical to the EA's own writer (System\\AltDataFetch.mqh
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RebuildFeatures/FeatureValue) - change BOTH together, append-only. The EA's
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catalog is broader (indices, more FX, energy, crypto); it is authoritative for
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production, this exporter only covers the symbols research screens.
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Format (semicolon-separated, header row, chronological):
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date;feat1;feat2;...
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2010.07.24;0.1234;-0.0567;...
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Every row is as-of its DATE at 00:00: built only from source rows with
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published <= that date. The EA joins each D1 bar to the last row <= bar open.
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A sidecar {SYM}_D1.meta carries the feature list + build stamp for staleness
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checks on the EA side.
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Usage:
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python -m altdata.export
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"""
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import datetime as dt
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from .common import DATA_ROOT
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from .screen import cot_features, fred_features
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EA_DIR = Path(r"C:\Users\admin\AppData\Roaming\MetaQuotes\Terminal\Common\Files\Warrior_EA\AltData")
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# symbol -> feature names (order = EA input order, append-only). Mirror of the
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# EA's symbol catalog in System\AltDataFetch.mqh BuildCatalog(). Policy 2026-08-16
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# (user, twice): every feature the sources can serve is wired - screens are priors,
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# not gates. IVOL[sym] names the instrument's own implied-vol FRED series.
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EIA_BLOCK = ["eia_stk_idx1y", "eia_stk_chg4", "eia_util"]
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RISK_BLOCK = ["vix_chg5", "vix", "usd_chg5"]
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COT_BLOCK = ["cot_idx_1y", "cot_idx_3y", "cot_chg_4w"]
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MAC_BLOCK = ["mac_y10", "mac_curve", "mac_bei", "mac_gap", "mac_cpi", "mac_unemp"]
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IVOL = {"XAUUSD": "GVZCLS", "XTIUSD": "OVXCLS"}
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SURVIVORS = {
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"SP500": RISK_BLOCK + ["cot_spec_net"] + EIA_BLOCK + MAC_BLOCK,
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"USDJPY": COT_BLOCK + RISK_BLOCK + EIA_BLOCK + MAC_BLOCK,
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"XAUUSD": RISK_BLOCK + ["ivol_chg5", "ivol"] + EIA_BLOCK + MAC_BLOCK,
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"EURUSD": COT_BLOCK + RISK_BLOCK + EIA_BLOCK + MAC_BLOCK,
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"XTIUSD": RISK_BLOCK + ["ivol_chg5", "ivol"] + EIA_BLOCK + MAC_BLOCK,
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}
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# Fixed A-PRIORI scale constants (units-based, never fitted to data - fitting
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# them would leak the sample's distribution into every bar). Goal: values
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# land roughly in the +/-1 band the EA's other feature blocks occupy, so the
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# first BN layer sees nothing exotic. MI is invariant to these transforms.
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TRANSFORMS = {
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"vix": lambda v: v / 100.0, # 10..80 -> 0.1..0.8
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"vix_chg5": lambda v: v / 10.0, # +/-30 spikes -> +/-3
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"usd_chg5": lambda v: v, # broad-index 5d change, ~+/-3
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"cot_spec_net": lambda v: v, # net/OI, already +/-0.5
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"cot_comm_net": lambda v: v,
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"cot_idx_1y": lambda v: v - 0.5, # percentile 0..1 -> +/-0.5
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"cot_idx_3y": lambda v: v - 0.5,
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"cot_chg_4w": lambda v: v, # net/OI 4w delta, ~+/-0.2
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"eia_stk_idx1y": lambda v: v - 0.5, # percentile 0..1 -> +/-0.5
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"eia_stk_chg4": lambda v: v * 10.0, # 4w fractional change ~+/-0.03 -> +/-0.3
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"eia_util": lambda v: (v - 90.0) / 10.0, # utilization % ~80..98 -> +/-1
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"ivol": lambda v: v / 100.0, # instrument IV level, same scale as vix
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"ivol_chg5": lambda v: v / 10.0,
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"mac_y10": lambda v: v, # 20-obs yield change, ~+/-0.8
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"mac_curve": lambda v: v, # slope in pct-points, ~-1..3
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"mac_bei": lambda v: v, # 20-obs breakeven change, ~+/-0.5
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"mac_gap": lambda v: v / 10.0, # Fed-ECB differential, -2..5 -> -0.2..0.5
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"mac_cpi": lambda v: v * 10.0, # yoy fraction 0..0.09 -> 0..0.9
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"mac_unemp": lambda v: v / 10.0, # 12m change in pp; COVID +10 -> +1
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}
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def ivol_features(sym: str):
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sid = IVOL.get(sym)
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if not sid:
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return []
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f = pd.read_csv(DATA_ROOT / "fred" / f"{sid}.csv",
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parse_dates=["observed", "published"]).sort_values("observed")
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v = f["value"].reset_index(drop=True)
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return [("ivol", pd.DataFrame({"published": f["published"].reset_index(drop=True),
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"ivol": v, "ivol_chg5": v - v.shift(5)}))]
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def mac_features():
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"""US macro block; identical observation-index arithmetic to the EA's writer."""
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def fred(sid):
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f = pd.read_csv(DATA_ROOT / "fred" / f"{sid}.csv",
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parse_dates=["observed", "published"]).sort_values("observed")
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return f.reset_index(drop=True)
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out = []
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y10 = fred("DGS10")
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out.append(("mac_y10", pd.DataFrame({"published": y10["published"],
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"mac_y10": y10["value"] - y10["value"].shift(20)})))
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cur = fred("T10Y2Y")
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out.append(("mac_curve", pd.DataFrame({"published": cur["published"],
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"mac_curve": cur["value"]})))
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bei = fred("T5YIE")
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out.append(("mac_bei", pd.DataFrame({"published": bei["published"],
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"mac_bei": bei["value"] - bei["value"].shift(20)})))
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# policy gap: each leg joined on its own publication date, differenced on the day grid
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dff, ecb = fred("DFF"), fred("ECBDFR")
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out.append(("_dff", pd.DataFrame({"published": dff["published"], "_dff": dff["value"]})))
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out.append(("_ecb", pd.DataFrame({"published": ecb["published"], "_ecb": ecb["value"]})))
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cpi = fred("CPIAUCNS")
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out.append(("mac_cpi", pd.DataFrame({"published": cpi["published"],
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"mac_cpi": cpi["value"] / cpi["value"].shift(12) - 1.0})))
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un = fred("UNRATE")
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out.append(("mac_unemp", pd.DataFrame({"published": un["published"],
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"mac_unemp": un["value"] - un["value"].shift(12)})))
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return out
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def eia_features():
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"""Weekly WPSR feature frames, same publication stamps as eia.py wrote them.
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Untransformed here (TRANSFORMS applies the fixed constants), matching how
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cot/fred features arrive. Missing collector output -> skip with a warning
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so the price-complex features still export."""
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eia_dir = DATA_ROOT / "eia"
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out = []
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try:
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stk = pd.read_csv(eia_dir / "crude_stocks_ex_spr.csv",
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parse_dates=["observed", "published"]).sort_values("observed")
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stk = stk.reset_index(drop=True)
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f = pd.DataFrame({"published": stk["published"]})
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# pandas average-rank pct of the LAST window element = EA RollingPctRank
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f["eia_stk_idx1y"] = stk["value"].rolling(52, min_periods=26).apply(
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lambda w: w.rank(pct=True).iloc[-1], raw=False)
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f["eia_stk_chg4"] = stk["value"] / stk["value"].shift(4) - 1.0
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out.append(("eia_stk", f))
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util = pd.read_csv(eia_dir / "refinery_utilization_pct.csv",
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parse_dates=["observed", "published"]).sort_values("observed")
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out.append(("eia_util", pd.DataFrame({"published": util["published"],
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"eia_util": util["value"]})))
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except FileNotFoundError as exc:
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print(f"WARNING: EIA collector output missing ({exc}) - EIA columns will be empty")
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return out
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def daily_panel(sym: str) -> pd.DataFrame:
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"""One row per calendar day 2010->today; each column as-of that day."""
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days = pd.date_range("2010-01-01", dt.date.today(), freq="D").astype("datetime64[ns]")
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panel = pd.DataFrame(index=days)
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sources = []
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cot = cot_features(sym)
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if cot is not None:
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sources.append(cot)
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sources.extend(f for _, f in fred_features())
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sources.extend(f for _, f in eia_features())
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sources.extend(f for _, f in ivol_features(sym))
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sources.extend(f for _, f in mac_features())
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for src in sources:
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src = src.sort_values("published").dropna()
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src["published"] = src["published"].astype("datetime64[ns]")
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cols = [c for c in src.columns if c != "published"]
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joined = pd.merge_asof(pd.DataFrame({"day": days}), src,
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left_on="day", right_on="published",
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direction="backward")
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for c in cols:
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if c not in panel.columns:
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panel[c] = joined[c].to_numpy()
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if "_dff" in panel.columns and "_ecb" in panel.columns:
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panel["mac_gap"] = panel["_dff"] - panel["_ecb"]
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panel.drop(columns=["_dff", "_ecb"], inplace=True)
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return panel
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def main() -> None:
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EA_DIR.mkdir(parents=True, exist_ok=True)
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stamp = dt.datetime.now(dt.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
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for sym, feats in SURVIVORS.items():
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panel = daily_panel(sym)
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missing = [f for f in feats if f not in panel.columns]
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if missing:
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# Keep the column (empty) rather than narrowing the CSV: the header must
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# stay identical to the EA writer's or the name pin flags a mismatch.
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print(f"WARNING: {sym}: no source data for {missing} - exporting empty columns")
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for f in missing:
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panel[f] = np.nan
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out = panel[feats].dropna(how="all")
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for f in feats:
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out[f] = TRANSFORMS[f](out[f])
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lines = ["date;" + ";".join(feats)]
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for day, row in out.iterrows():
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vals = ";".join("" if np.isnan(v) else f"{v:.6f}" for v in row)
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lines.append(f"{day:%Y.%m.%d};{vals}")
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dest = EA_DIR / f"{sym}_D1.csv"
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dest.write_text("\n".join(lines) + "\n", encoding="ascii")
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meta = EA_DIR / f"{sym}_D1.meta"
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meta.write_text(f"features={len(feats)}\nnames={','.join(feats)}\n"
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f"built={stamp}\nrows={len(out)}\n", encoding="ascii")
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print(f"{sym}: {len(out)} daily rows x {len(feats)} features -> {dest}")
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if __name__ == "__main__":
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main()
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