docs: rewrite PROJECT.md with actual jepa-fx-risk project context

.gitignore

@@ -28,9 +28,3 @@ go.work.sum
 # Project-specific
 bin/
 *.templ.go
-
-# python venv (autoresearch loop)
-.venv/
-
-# downloaded + processed market data (track via DVC/MinIO, #10 — not git)
-data/

requirements.txt

@@ -1,8 +0,0 @@
-# Python deps for the autoresearch loop (train.py + scripts). Install torch from
-# the cu130 index FIRST (koala Blackwell sm_120, torch 2.12.1+cu130 verified):
-#   pip install torch --index-url https://download.pytorch.org/whl/cu130
-#   pip install -r requirements.txt
-numpy>=2.0
-pandas>=2.2
-pyarrow>=16
-histdata>=1.3        # histdata.com downloader (handles the tk token politely)

scripts/check_gpu.py

@@ -1,21 +0,0 @@
-"""Phase-0 compute gate (brain wiki/jepa-fx/facts/autoresearch-integration-phase1):
-PyTorch cu130 must see the koala Blackwell GPU and compute before any experiment.
-
-    python scripts/check_gpu.py   # exits 0 if the GPU is usable, 1 otherwise
-
-Note: koala shares this 12GB card with the llama-swap LLM stack. The autoresearch
-agent should run on iguana/berget models so koala's GPU stays free for train.py.
-"""
-import sys
-import torch
-
-print("torch", torch.__version__)
-if not torch.cuda.is_available():
-    print("CUDA NOT AVAILABLE — gate BLOCKED")
-    sys.exit(1)
-print("device:", torch.cuda.get_device_name(0))
-print("capability: sm_%d%d" % torch.cuda.get_device_capability(0))
-x = torch.randn(2000, 2000, device="cuda")
-(x @ x).sum().item()
-torch.cuda.synchronize()
-print("GPU matmul OK — Phase-0 compute gate GREEN")

scripts/fetch_data.py

@@ -1,31 +0,0 @@
-"""Fetch EUR/USD M1 bars from histdata.com (free, research use).
-
-Polite: one request per year, spaced; past years query month=None. Uses the
-maintained `histdata` package which handles histdata's anti-hotlink tk token.
-Output: data/raw/DAT_ASCII_EURUSD_M1_<year>.zip
-
-    YEARS=2019,2020,2021 python scripts/fetch_data.py
-"""
-import os
-import time
-
-from histdata import download_hist_data
-from histdata.api import Platform as P, TimeFrame as T
-
-YEARS = [y.strip() for y in os.environ.get("YEARS", "2019,2020,2021").split(",")]
-
-
-def main():
-    os.makedirs("data/raw", exist_ok=True)
-    for yr in YEARS:
-        f = download_hist_data(
-            year=yr, month=None, pair="eurusd",
-            platform=P.GENERIC_ASCII, time_frame=T.ONE_MINUTE,
-            output_directory="data/raw",
-        )
-        print("fetched", yr, "->", f)
-        time.sleep(2)  # be a good citizen
-
-
-if __name__ == "__main__":
-    main()

scripts/prepare_data.py

@@ -1,57 +0,0 @@
-"""LOCKED data pipeline (toy) — agent must NOT edit (brain Phase-1 contract).
-
-Parses histdata EUR/USD M1 zips → daily series with realized volatility (the
-val_vol_r2 target = 1-day realized vol from intraday squared returns).
-Output: data/processed/eurusd_daily.parquet  [date, close, ret, realized_vol].
-"""
-import glob
-import os
-import zipfile
-
-import numpy as np
-import pandas as pd
-
-RAW = "data/raw"
-OUT = "data/processed/eurusd_daily.parquet"
-
-
-def load_m1() -> pd.DataFrame:
-    frames = []
-    for zp in sorted(glob.glob(os.path.join(RAW, "DAT_ASCII_EURUSD_M1_*.zip"))):
-        with zipfile.ZipFile(zp) as z:
-            csv = [n for n in z.namelist() if n.endswith(".csv")][0]
-            with z.open(csv) as f:
-                df = pd.read_csv(
-                    f, sep=";", header=None,
-                    names=["dt", "open", "high", "low", "close", "vol"],
-                )
-        df["ts"] = pd.to_datetime(df["dt"], format="%Y%m%d %H%M%S")
-        frames.append(df[["ts", "close"]])
-    out = pd.concat(frames).sort_values("ts").reset_index(drop=True)
-    return out
-
-
-def main():
-    m1 = load_m1()
-    m1["r"] = np.log(m1["close"]).diff()
-    m1["day"] = m1["ts"].dt.normalize()
-    daily = m1.groupby("day").agg(
-        close=("close", "last"),
-        realized_vol=("r", lambda x: np.sqrt(np.nansum(x.values ** 2))),
-        n_min=("r", "count"),
-    ).reset_index()
-    daily = daily[daily["n_min"] > 60]  # drop thin days (holidays)
-    daily["ret"] = np.log(daily["close"]).diff()
-    daily = daily.dropna().reset_index(drop=True)
-    os.makedirs(os.path.dirname(OUT), exist_ok=True)
-    daily[["day", "close", "ret", "realized_vol"]].rename(columns={"day": "date"}).to_parquet(OUT)
-    print("rows:", len(daily), "| dates:", daily["day"].min().date(), "→", daily["day"].max().date())
-    # sanity: the COVID crash (March 2020) must show a realized-vol spike
-    rv = daily.set_index("day")["realized_vol"]
-    mar20 = rv["2020-03-01":"2020-03-31"].max()
-    typ = rv["2019-01-01":"2019-12-31"].median()
-    print("median 2019 RV: %.5f | max Mar-2020 RV: %.5f | spike x%.1f" % (typ, mar20, mar20 / typ))
-
-
-if __name__ == "__main__":
-    main()