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merge: B1 — 6 paper-track strategies as Dagster assets + Tiingo equity data

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Faithful ports (sixtyforty/multistrat/gd/funding/crossvenue/poc incl. torch), generic ForwardTracker,
6 assets wired into cockpit_forward, equity sleeves on Tiingo native adjClose (33yr total-return).
327 tests green, mypy clean.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-06-16 08:59:39 +02:00
parent 3e57f2db01 c192010c36
commit 98ce7b3c2b
30 changed files with 2675 additions and 48 deletions
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35
docs/superpowers/plans/2026-06-16-lifecycle-b1-paper-tracks.md
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@@ -624,33 +624,34 @@ class FakeBinanceFunding:
---
## Task 8: CryptoPerpBarClient + VolIndexClient adapters + poc strategy
## Task 8: poc strategy — FAITHFUL port WITH torch (vendor the real helpers)
**Source:** foxhunt `scripts/surfer/surfer_poc.py` `paper(cfg)` path ONLY + `compute_weights`, `xs_weights` (numpy), `vrp_ccy`/`vrp_book_series`. Momentum sleeve: residual (market-beta-stripped) 20-day momentum → cross-sectional z-score market-neutral unit-gross weights → top-30 → EWMA smooth → weekly rebalance (`day % 7 == 0`). VRP sleeve: Deribit DVOL short-vol with tail gates (don't sell into rising vol over `vrp_rise_k=5`; full size only below `vrp_level_win=60` median), scaled to momentum vol, blended at `vrp_risk_frac=0.30`. **Calibration deviation (per spec):** derive `vrp_mu` (momentum daily vol) + `vrp_vu` (VRP daily vol) from the live panel's own lookback window, NOT `pit_sweep`. NO torch. Live-booking; `extra` holds `positions`, `prices`, `vrp_equity`, `vrp_mu`, `vrp_vu`. Emit days-list (`ret = comb_ret`).
**CORRECTION (do not dodge deps):** poc is ported **faithfully** — keep the algorithm 1:1. Use **torch**; use the **real** `signal_sweep` (`xs_weights`, `validate`, `sharpe_t`) and **real** `pit_sweep` (`load()`); keep the **real** `_vrp_calib` (PIT-history-based VRP sizing). Do NOT reimplement any of this in numpy and do NOT change the calibration source — changing an algorithm to avoid a package is not allowed. The only wrapping is persistence: the live mark/rebalance computation is unchanged; the tracker books `comb_ret` into days-list and the bespoke poc fields live in `extra`.
**Source:** foxhunt `scripts/surfer/surfer_poc.py` (`paper(cfg)`, `compute_weights`, `vrp_ccy`/`vrp_book_series`, `_vrp_calib`, `_live_panel`), `scripts/surfer/signal_sweep.py`, `scripts/surfer/pit_sweep.py`.
**Files:**
- Create: `src/fxhnt/adapters/data/crypto_perp.py` (`CryptoPerpBarClient`)
- Create: `src/fxhnt/adapters/data/vol_index.py` (`VolIndexClient`)
- Create: `src/fxhnt/domain/strategies/momentum_vrp.py` (pure: `xs_weights`, `compute_weights`, `vrp_series`, `combined_return`)
- Modify: `src/fxhnt/application/paper_strategies.py` (add `MomentumVrpStrategy`)
- Modify: `tests/integration/test_paper_strategies.py`
- Create: `src/fxhnt/vendor/surfer/__init__.py`, `signal_sweep.py`, `pit_sweep.py`, `surfer_poc.py` — copied VERBATIM from foxhunt `scripts/surfer/` (preserve the algorithm). Only allowed edits: make the state path + the PIT-cache path configurable via env/param instead of the hardcoded `_REPO`-relative paths (so they resolve to `/data/surfer` + the pod's PIT cache); keep all math, torch usage, and the `from signal_sweep/pit_sweep import ...` wiring intact (adjust to package-relative imports).
- Create: `src/fxhnt/adapters/data/crypto_perp.py` (`CryptoPerpBarClient`) + `src/fxhnt/adapters/data/vol_index.py` (`VolIndexClient`) — only if the vendored `_live_panel`/`_dvol` are refactored to take injected clients; otherwise the vendored module keeps its own urllib fetch (faithful) and these ports are skipped. Prefer the faithful path: keep the vendored fetch.
- Modify: `src/fxhnt/application/paper_strategies.py` (add `MomentumVrpStrategy` wrapping the vendored poc paper step).
- Modify: `pyproject.toml` (add `torch` to the orchestration/poc extra) and `infra/docker/fxhnt.Dockerfile` (install the extra so the cockpit image has torch). torch CPU wheel is sufficient (paper inference is tiny).
- Modify: `tests/integration/test_paper_strategies.py`.
- [ ] **Step 1: Write the failing tests** (pure-domain, deterministic — no network):
- `test_xs_weights_market_neutral_unit_gross`: random `sig` → `xs_weights(sig)` rows sum to ≈0 (market-neutral) and `sum|w|` ≈ 1 (unit gross).
- `test_compute_weights_topk_and_neutral`: synthetic panel of 50 perps over 80 days → returned weights have ≤`topk=30` nonzeros per row, row-sum ≈ 0.
- `test_vrp_tail_gate_holds_when_vol_rising`: a DVOL series that is strictly rising → `vrp_series` books 0 (gated), and below-median falling vol → nonzero short-vol return.
- `test_momentum_vrp_combined_return`: fixed positions/prices + one new bar + a DVOL point → `comb_ret == (1-f)*mom_ret + f*scaled` with hand-computed `mom_ret` (price change − funding) and `scaled = (dvol_today/vu)*mu`.
- [ ] **Step 1: Vendor the real modules** — copy `signal_sweep.py`, `pit_sweep.py`, `surfer_poc.py` from foxhunt `scripts/surfer/` into `src/fxhnt/vendor/surfer/`. Convert the `sys.path.insert` + bare `from signal_sweep import ...` / `import pit_sweep` to package-relative imports (`from fxhnt.vendor.surfer.signal_sweep import ...`). Make `STATE`, the dvol cache path, and the pit-cache path read from env (`FXHNT_SURFER_DATA_DIR`, default `/data/surfer`; PIT cache path via the existing pit_sweep config) — NO other edits. Keep `import torch`.
- [ ] **Step 2: Run to verify they fail** → FAIL.
- [ ] **Step 2: Write the failing test** (uses the REAL functions; torch on CPU; small fixtures):
- `test_xs_weights_market_neutral_unit_gross`: real `signal_sweep.xs_weights` on a small `sig` → rows sum ≈0, `sum|w|` ≈1.
- `test_compute_weights_shape_and_neutral`: real `surfer_poc.compute_weights` on a synthetic 50-perp × 80-day panel → ≤`topk` nonzeros/row, row-sum ≈0.
- `test_momentum_vrp_service_round_trips`: drive `MomentumVrpStrategy` over a fixed local panel/DVOL fixture (monkeypatch the vendored fetch to return fixtures so no network) through two `ForwardTracker` steps → second step books one row; `ForwardStateReader().read(...)` returns a days-list summary with `days==1`; `extra` carries `positions`/`vrp_*`.
- [ ] **Step 3: Implement the domain** `momentum_vrp.py` — port `xs_weights` (numpy z-score, market-neutral, unit gross), `compute_weights(close, qvol, days, cfg)` (residual momentum, top-K, EWMA smooth, weekly held weights), `vrp_ccy`/`vrp_series` (tail-gated short-vol per currency, combine BTC+ETH), and `combined_return(prev_positions, prev_prices, last_close, funding_now, dvol_today, mu, vu, f)`. Reimplement `sharpe`/`std` helpers in numpy. `cfg` defaults from source `CFG`.
- [ ] **Step 3: Run to verify they fail** → FAIL.
- [ ] **Step 4: Implement the adapters** — `crypto_perp.py`: Binance `/fapi/v1/exchangeInfo` (top-liquid perps by `/ticker/24hr` volume) + `/fapi/v1/klines?interval=1d` panel + `/fapi/v1/premiumIndex` funding → `panel()`. `vol_index.py`: Deribit `https://www.deribit.com/api/v2/public/get_volatility_index_data?currency={ccy}&...` → `{epoch_day: dvol}` (cache to `/data/surfer/dvol_{ccy}.json` like the original `_dvol`).
- [ ] **Step 4: Implement the service** `MomentumVrpStrategy.advance(last_date, extra)` — call the vendored poc mark/rebalance logic (the real `paper`-equivalent, refactored to return values rather than print): seed its working state from `extra`; compute `comb_ret` exactly as `surfer_poc.paper` does (momentum mark + real `_vrp_calib`-sized VRP sleeve); return `([(iso(today), comb_ret)], {positions, prices, vrp_equity, vrp_mu, vrp_vu})`. Convert epoch-day → ISO date for the days-list. The numbers must match the vendored algorithm exactly.
- [ ] **Step 5: Implement the service** `MomentumVrpStrategy.advance(last_date, extra)` — fetch panel + DVOL; on first call (no `vrp_vu` in extra) calibrate `mu`/`vu` from the panel lookback; MARK held book → `comb_ret`; weekly rebalance to `compute_weights` target; convert epoch-day → ISO date; return `([(iso_today, comb_ret)], {positions, prices, vrp_equity, vrp_mu, vrp_vu})`.
- [ ] **Step 5: Image deps** — add `torch` to the relevant extra in `pyproject.toml`; update `infra/docker/fxhnt.Dockerfile` to install it; ensure the PIT cache `pit_sweep.load()` needs is present in the cockpit image or mounted (point `pit_sweep` at the warehouse/PVC path available to the Dagster pod). Note this as the one infra add for B1.
- [ ] **Step 6: Run to verify it passes** — pytest + mypy → PASS.
- [ ] **Step 7: Commit** — `git commit -m "feat(b1): surfer poc momentum+VRP paper-forward strategy (numpy, no torch)"`
- [ ] **Step 7: Commit** — `git commit -m "feat(b1): surfer poc paper-forward strategy — faithful port with torch + real signal_sweep/pit_sweep"`
---

8
docs/superpowers/specs/2026-06-15-lifecycle-b1-paper-tracks-design.md
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@@ -5,8 +5,12 @@
**Scope of THIS increment:** all 6 remaining strategies — `funding`, `crossvenue`, `multistrat`, `sixtyforty`,
`gd`, and `poc`. After this increment all 7 registry strategies have live forward NAV in the cockpit.
`poc` was briefly going to be a separate B1b increment, but its paper-forward path turns out to be numpy-only
(no torch, no `pit_sweep` — see the poc subsection), so it folds cleanly into this increment.
`poc` was briefly going to be a separate B1b increment; it folds into this increment. **CORRECTION
(2026-06-16):** poc is ported **faithfully with torch** + the real `signal_sweep`/`pit_sweep` + the real
`_vrp_calib`. An earlier draft proposed a numpy reimplementation that dropped torch and changed the VRP
calibration source — that is rejected: you do not change an algorithm to avoid a package. The image gains
torch; the helpers are vendored verbatim. See plan Task 8 for the authoritative approach (it supersedes the
"numpy-only / no torch / calibration deviation" wording in the poc subsection + Out-of-scope below).
## Goal

8
infra/docker/fxhnt.Dockerfile
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@@ -5,7 +5,13 @@ ENV PYTHONUNBUFFERED=1 PIP_NO_CACHE_DIR=1
WORKDIR /app
COPY pyproject.toml README.md ./
COPY src/ ./src/
RUN pip install --upgrade pip && pip install '.[web,pg,data,factory,orchestration]'
# The `poc` extra pulls torch (faithful surfer-PoC port requirement). Use the PyTorch CPU index so the
# cockpit pod gets the small CPU wheel, not the multi-GB CUDA build (no GPU here). NOTE (deploy): the surfer
# PoC paper track also needs the crypto PIT cache at $FXHNT_SURFER_DATA_DIR/crypto_pit/*.npz — already present
# in the pod for the combined book; no extra mount needed.
RUN pip install --upgrade pip && \
pip install --extra-index-url https://download.pytorch.org/whl/cpu \
'.[web,pg,data,factory,orchestration,poc]'
# DSN is injected at runtime via secretKeyRef in the K8s manifests; empty here so the image contains no credentials.
ENV FXHNT_OPERATIONAL_DSN=""
EXPOSE 8080

2
infra/k8s/orchestration/dagster.yaml
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@@ -109,6 +109,8 @@ spec:
valueFrom: { secretKeyRef: { name: db-credentials, key: password } }
- name: DATABENTO_API_KEY
valueFrom: { secretKeyRef: { name: databento-credentials, key: api-key } }
- name: TIINGO_API_KEY # equity sleeves (sixtyforty/multistrat/gd) fetch Tiingo adjClose
valueFrom: { secretKeyRef: { name: tiingo-api, key: key } }
readinessProbe: { tcpSocket: { port: 3000 }, initialDelaySeconds: 10, periodSeconds: 15 }
volumeMounts:
- { name: dagster-home, mountPath: /dagster-home }

19
pyproject.toml
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@@ -22,6 +22,17 @@ web = ["fastapi>=0.110", "uvicorn[standard]>=0.29", "jinja2>=3.1", "httpx>=0.27"
pg = ["psycopg[binary]>=3.1"]
factory = ["scipy>=1.11"]
orchestration = ["dagster>=1.12,<1.13", "dagster-webserver>=1.12,<1.13", "dagster-postgres>=0.28,<0.29"]
# Surfer PoC paper-track (vendored crypto XS-momentum + VRP). torch is a faithful-port requirement
# (signal_sweep.sharpe_t/validate + surfer_poc.backtest use it) — CPU wheel only (no GPU in the cockpit pod).
poc = ["torch>=2.2"]
[tool.uv.sources]
torch = { index = "pytorch-cpu" }
[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true
[project.scripts]
fxhnt = "fxhnt.cli:app"
@@ -48,6 +59,14 @@ select = ["E", "F", "I", "UP", "B", "SIM"]
python_version = "3.11"
strict = true
ignore_missing_imports = true
# Vendored third-party code (faithful verbatim copies, only import/path edits) is intentionally untyped —
# annotating it would diverge from upstream. Exclude it from strict checking; callers treat it as Any.
exclude = '^src/fxhnt/vendor/'
[[tool.mypy.overrides]]
module = "fxhnt.vendor.*"
ignore_errors = true
follow_imports = "skip"
[tool.pytest.ini_options]
pythonpath = ["src"]

86
src/fxhnt/adapters/data/binance_funding.py Normal file
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@@ -0,0 +1,86 @@
"""urllib BinanceFundingClient — Binance USDⓈ-M futures public API (no key, no capital). Faithful to
foxhunt scripts/surfer/crypto_funding_paper.py's scan() (lines 48-92): the hedgeable, liquid,
crypto-native universe + trailing-30d-mean funding (tf30) + today's booking rate (last24).
* crypto_native() -> symbols with underlyingType == "COIN" from /fapi/v1/exchangeInfo
* spot_symbols() -> symbols with a Binance SPOT market (api.binance.com/api/v3/ticker/price)
* universe() -> {sym: quoteVolume} for crypto-native, spot-hedgeable USDT perps with vol > LIQ_USD
* scan(prev) -> (liq, tf30, last24) over union of the universe and prev positions
Retries with backoff; mirrors the original `get()` helper and the yahoo_daily `_get` style."""
from __future__ import annotations
import json
import time
import urllib.request
from typing import Any
from fxhnt.domain.strategies.funding_carry import LIQ_USD
_FAPI = "https://fapi.binance.com"
INTERVALS_PER_DAY = 3 # Binance funding settles every 8h (matches the original constant)
class BinanceFundingClient:
def __init__(self, liq_usd: float = LIQ_USD) -> None:
self._liq_usd = liq_usd
def _get(self, url: str, tries: int = 4) -> Any:
for a in range(tries):
try:
req = urllib.request.Request(url, headers={"User-Agent": "curl/8"})
return json.loads(urllib.request.urlopen(req, timeout=30).read())
except Exception: # noqa: BLE001 — transient HTTP; retry then re-raise
if a == tries - 1:
raise
time.sleep(2 * (a + 1))
raise RuntimeError("unreachable")
def crypto_native(self) -> set[str]:
"""Symbols whose underlyingType is COIN (original crypto_native(), line 48-50)."""
info = self._get(f"{_FAPI}/fapi/v1/exchangeInfo")
return {s["symbol"] for s in info["symbols"] if s.get("underlyingType") == "COIN"}
def spot_symbols(self) -> set[str]:
"""Symbols with a Binance SPOT market — required to build the delta-neutral hedge
(original spot_symbols(), line 53-57). Perp-only coins can't be cash-and-carry harvested."""
return {x["symbol"] for x in self._get("https://api.binance.com/api/v3/ticker/price")}
def universe(self) -> dict[str, float]:
"""Liquid, crypto-native, HEDGEABLE (spot+perp) USDT perps (original universe(), line 60-68)."""
native = self.crypto_native()
spot = self.spot_symbols()
t = self._get(f"{_FAPI}/fapi/v1/ticker/24hr")
return {
x["symbol"]: float(x["quoteVolume"])
for x in t
if x["symbol"].endswith("USDT")
and x["symbol"] in native
and x["symbol"] in spot
and float(x["quoteVolume"]) > self._liq_usd
}
def _funding_hist(self, sym: str, limit: int = 90) -> list[float]:
"""Funding-rate history (original funding_hist(), line 71-73)."""
h = self._get(f"{_FAPI}/fapi/v1/fundingRate?symbol={sym}&limit={limit}")
return [float(x["fundingRate"]) for x in h]
def scan(self, prev: set[str]) -> tuple[dict[str, float], dict[str, float], dict[str, float]]:
"""(liq, tf30, last24) for the union of the universe and prev positions (original scan(),
line 76-92)."""
liq = self.universe()
need = set(liq) | set(prev)
tf30: dict[str, float] = {}
last24: dict[str, float] = {}
for i, sym in enumerate(sorted(need)):
try:
h = self._funding_hist(sym)
except Exception: # noqa: BLE001 — skip transient per-symbol failures
continue
if len(h) < 30:
continue
tf30[sym] = (sum(h) / len(h)) * INTERVALS_PER_DAY
last24[sym] = sum(h[-INTERVALS_PER_DAY:])
if i % 50 == 49:
time.sleep(0.5)
return liq, tf30, last24

109
src/fxhnt/adapters/data/cross_venue.py Normal file
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@@ -0,0 +1,109 @@
"""urllib CrossVenueFundingClient — Binance / Bybit / Hyperliquid public perp APIs (no key, no capital).
Faithful to foxhunt scripts/surfer/cross_venue_funding.py (binance()/bybit()/hyperliquid()/spreads(),
lines 55-98): each venue's funding is normalized to a DAILY rate, then exposed per coin per venue.
Per-venue daily-funding normalization (must match the original exactly):
* Binance: lastFundingRate × (24 / fundingIntervalHours) (default 8h interval → ×3) [lines 56-62]
* Bybit: fundingRate × 3 (8h interval → ×3) [lines 65-68]
* Hyperliquid: funding × 24 (1h interval → ×24) [lines 71-78]
`base(sym)` strips a trailing USDT/USDC quote so the same coin maps across venues (Hyperliquid already
uses the bare coin name). snapshot() returns (funding, volume) — each {coin: {venue: rate|vol}} from ONE
fetch — over the union of coins present on any fetched venue (venues that fail to fetch are simply absent,
as in spreads()).
Retries with backoff; mirrors the original `get()` helper (GET + POST for Hyperliquid)."""
from __future__ import annotations
import json
import time
import urllib.request
from typing import Any
_BINANCE = "https://fapi.binance.com"
_BYBIT = "https://api.bybit.com"
_HL = "https://api.hyperliquid.xyz"
_VENUES = ("Binance", "Bybit", "HL")
def _base(sym: str) -> str:
"""Strip a trailing USDT/USDC quote (original base(), lines 48-52)."""
for q in ("USDT", "USDC"):
if sym.endswith(q):
return sym[: -len(q)]
return sym
class CrossVenueFundingClient:
def _get(self, url: str, post: dict[str, Any] | None = None, tries: int = 4) -> Any:
data = json.dumps(post).encode() if post is not None else None
headers = {"User-Agent": "Mozilla/5.0", "Accept": "application/json"}
if post is not None:
headers["Content-Type"] = "application/json"
for a in range(tries):
try:
req = urllib.request.Request(url, data=data, headers=headers)
return json.loads(urllib.request.urlopen(req, timeout=30).read())
except Exception: # noqa: BLE001 — transient HTTP; retry then re-raise
if a == tries - 1:
raise
time.sleep(2 * (a + 1))
raise RuntimeError("unreachable")
def _binance(self) -> dict[str, tuple[float, float]]:
"""{coin: (daily_funding, 24h_quote_volume)} (original binance(), lines 55-62)."""
fund = {x["symbol"]: float(x["lastFundingRate"]) for x in self._get(f"{_BINANCE}/fapi/v1/premiumIndex")}
try:
itv = {
x["symbol"]: float(x.get("fundingIntervalHours", 8))
for x in self._get(f"{_BINANCE}/fapi/v1/fundingInfo")
}
except Exception: # noqa: BLE001 — fundingInfo optional; default 8h interval
itv = {}
vol = {x["symbol"]: float(x["quoteVolume"]) for x in self._get(f"{_BINANCE}/fapi/v1/ticker/24hr")}
return {
_base(s): (f * (24.0 / itv.get(s, 8)), vol.get(s, 0.0))
for s, f in fund.items()
if s.endswith("USDT")
}
def _bybit(self) -> dict[str, tuple[float, float]]:
"""{coin: (daily_funding, 24h_turnover)} (original bybit(), lines 65-68)."""
r = self._get(f"{_BYBIT}/v5/market/tickers?category=linear")["result"]["list"]
return {
_base(x["symbol"]): (float(x["fundingRate"]) * 3, float(x.get("turnover24h", 0.0)))
for x in r
if x["symbol"].endswith("USDT") and x.get("fundingRate")
}
def _hyperliquid(self) -> dict[str, tuple[float, float]]:
"""{coin: (daily_funding, 24h_notional_volume)} (original hyperliquid(), lines 71-78)."""
r = self._get(f"{_HL}/info", post={"type": "metaAndAssetCtxs"})
meta, ctxs = r[0], r[1]
out: dict[str, tuple[float, float]] = {}
for u, c in zip(meta["universe"], ctxs, strict=True):
if c.get("funding") is not None:
out[u["name"]] = (float(c["funding"]) * 24, float(c.get("dayNtlVlm", 0.0)))
return out
def _fetch(self) -> dict[str, dict[str, tuple[float, float]]]:
"""{venue: {coin: (daily_funding, volume)}} — skip venues that fail (original spreads() lines 82-87)."""
fns = {"Binance": self._binance, "Bybit": self._bybit, "HL": self._hyperliquid}
out: dict[str, dict[str, tuple[float, float]]] = {}
for nm, fn in fns.items():
try:
out[nm] = fn()
except Exception: # noqa: BLE001 — a venue outage must not kill the scan (original prints & skips)
continue
return out
def snapshot(self) -> tuple[dict[str, dict[str, float]], dict[str, dict[str, float]]]:
"""One _fetch() pass per run → (funding_by_venue, volume_by_venue) built from the SAME response
objects (funding + the venue's volume field together), over the union of coins on the fetched
venues. Avoids the prior double-fetch + cross-snapshot drift of the two single-field methods."""
v = self._fetch()
coins = set().union(*[set(d) for d in v.values()]) if v else set()
funding = {c: {nm: v[nm][c][0] for nm in _VENUES if nm in v and c in v[nm]} for c in coins}
volume = {c: {nm: v[nm][c][1] for nm in _VENUES if nm in v and c in v[nm]} for c in coins}
return funding, volume

105
src/fxhnt/adapters/data/massive_daily.py Normal file
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@@ -0,0 +1,105 @@
"""urllib DailyBarClient — licensed Massive (formerly Polygon) daily closes with TOTAL-RETURN
reconstruction. Drop-in for YahooDailyClient on the equity sleeves (sixtyforty/multistrat/gd), which
compound daily TOTAL returns of dividend-paying ETFs (SPY/IEF/GLD/...).
Why reconstruction is required: Massive aggregates with `adjusted=true` are SPLIT-ONLY — dividends are
NOT folded in (a dividend-day price drop appears in full). So we rebuild a Yahoo-adjclose-equivalent
back-adjusted series ourselves from the split-adjusted closes + the dividends endpoint.
Method (standard back-adjustment): for each ex-dividend date D with cash C, multiply ALL closes on
dates strictly BEFORE D by factor (1 - C / close_{trading day immediately before D}). Factors compound
across dividends. The resulting day-over-day return on the ex-div day = raw_close_D / adj_close_{D-1} - 1
= price_return + C/prev_close, i.e. the dividend is added back (matches Yahoo's adjclose semantics).
Plan/history note: our Massive plan only entitles ~2 years of history; multi-year ranges 403. We request
a bounded RECENT window (default last ~500 calendar days, safely inside 2y) — far more than any sleeve's
signal lookback. Auth: Bearer with POLYGON_API_KEY (env). Host default https://api.massive.com.
"""
from __future__ import annotations
import datetime as dt
import json
import os
import time
import urllib.parse
import urllib.request
from typing import Any
_AGGS = "{base}/v2/aggs/ticker/{sym}/range/1/day/{frm}/{to}?adjusted=true&sort=asc&limit=50000"
_DIVS = "{base}/v3/reference/dividends?ticker={sym}&ex_dividend_date.gte={frm}&ex_dividend_date.lte={to}&limit=1000"
class MassiveDailyClient:
def __init__(
self,
api_key: str | None = None,
lookback_days: int = 500,
base_url: str = "https://api.massive.com",
) -> None:
self._key = api_key if api_key is not None else os.environ["POLYGON_API_KEY"]
self._lookback_days = lookback_days
self._base = base_url.rstrip("/")
def _get(self, url: str, tries: int = 4) -> dict[str, Any]:
for a in range(tries):
try:
req = urllib.request.Request(url, headers={"Authorization": f"Bearer {self._key}"})
data: dict[str, Any] = json.loads(urllib.request.urlopen(req, timeout=30).read())
return data
except Exception: # noqa: BLE001 — transient HTTP; retry then re-raise
if a == tries - 1:
raise
time.sleep(2 * (a + 1))
raise RuntimeError("unreachable")
def _window(self) -> tuple[str, str]:
to = dt.date.today()
frm = to - dt.timedelta(days=self._lookback_days)
return frm.isoformat(), to.isoformat()
def adj_closes(self, symbol: str) -> dict[str, float]:
frm, to = self._window()
sym = urllib.parse.quote(symbol)
aggs = self._get(_AGGS.format(base=self._base, sym=sym, frm=frm, to=to))
# split-adjusted closes keyed by date (tz-aware; midnight-UTC epoch ms)
closes: dict[str, float] = {}
for r in aggs.get("results") or []:
d = dt.datetime.fromtimestamp(r["t"] / 1000, dt.timezone.utc).strftime("%Y-%m-%d")
closes[d] = float(r["c"])
if not closes:
return {}
divs = self._get(_DIVS.format(base=self._base, sym=sym, frm=frm, to=to))
dividends: list[tuple[str, float]] = [
(r["ex_dividend_date"], float(r["cash_amount"]))
for r in (divs.get("results") or [])
if r.get("cash_amount")
]
return self._total_return(closes, dividends)
@staticmethod
def _total_return(closes: dict[str, float], dividends: list[tuple[str, float]]) -> dict[str, float]:
"""Back-adjust split-adjusted closes into a total-return series (Yahoo adjclose equivalent).
Accumulate one cumulative factor per date: for each ex-div date D (within the close window) with
cash C, every date strictly before D is multiplied by (1 - C / close_{trading day before D}).
"""
dates = sorted(closes)
factor: dict[str, float] = {d: 1.0 for d in dates}
for ex_date, cash in dividends:
# the last trading day strictly before the ex-div date
prior = [d for d in dates if d < ex_date]
if not prior:
continue # dividend predates our close window — no in-window dates to adjust
prev = prior[-1]
prev_close = closes[prev]
if prev_close <= 0:
continue
f = 1.0 - cash / prev_close
for d in prior:
factor[d] *= f
return {d: closes[d] * factor[d] for d in dates}

100
src/fxhnt/adapters/data/tiingo_daily.py Normal file
View File

@@ -0,0 +1,100 @@
"""urllib DailyBarClient — Tiingo native daily total-return closes. Drop-in for MassiveDailyClient on
the equity sleeves (sixtyforty/multistrat/gd), which compound daily TOTAL returns of dividend-paying
ETFs (SPY/IEF/GLD/PDBC/DBMF) plus crypto (BTC-USD in multistrat).
Why no reconstruction (unlike the Massive split-only adapter): Tiingo's equity/ETF `adjClose` is a
NATIVE total-return series (dividend AND split adjusted), so we read it directly. Tiingo carries ~33yr
of history; we still request a bounded RECENT window (default ~3yr) — far more than any sleeve's signal
lookback (≤126d warmup + forward) — to keep responses small.
Routing — crypto vs equity:
Tiingo serves crypto from a DIFFERENT endpoint with a DIFFERENT response shape. Crypto has no
dividends/splits, so its `close` IS the adjusted close (there is no `adjClose` field).
equity/ETF: GET /tiingo/daily/{ticker}/prices?startDate&endDate
→ flat JSON list of {date, close, adjClose, divCash, splitFactor, ...}; read `adjClose`.
crypto: GET /tiingo/crypto/prices?tickers={btcusd}&startDate&resampleFreq=1day
→ JSON list [{ticker, baseCurrency, quoteCurrency, priceData: [{date, close, ...}]}];
read nested `priceData[].close`. Yahoo-style `BTC-USD` is mapped to `btcusd`.
Auth: header `Authorization: Token <key>` with TIINGO_API_KEY (env). The key is used ONLY in the
request header — never logged or interpolated into a URL.
"""
from __future__ import annotations
import datetime as dt
import json
import os
import time
import urllib.parse
import urllib.request
from typing import Any
_DAILY = "{base}/tiingo/daily/{sym}/prices?startDate={frm}&endDate={to}&format=json"
_CRYPTO = "{base}/tiingo/crypto/prices?tickers={sym}&startDate={frm}&resampleFreq=1day&format=json"
# Known crypto bases (Yahoo-style `<BASE>-USD`). Tiingo routes these to the crypto endpoint.
_CRYPTO_BASES = {"BTC", "ETH", "SOL", "XRP", "ADA", "DOGE", "LTC", "BCH", "DOT", "AVAX", "LINK", "MATIC"}
class TiingoDailyClient:
def __init__(
self,
api_key: str | None = None,
lookback_days: int = 1100,
base_url: str = "https://api.tiingo.com",
) -> None:
self._key = api_key if api_key is not None else os.environ["TIINGO_API_KEY"]
self._lookback_days = lookback_days
self._base = base_url.rstrip("/")
def _get(self, url: str, tries: int = 4) -> Any:
for a in range(tries):
try:
# The key lives ONLY in this header — never in the URL or any log line.
req = urllib.request.Request(url, headers={"Authorization": f"Token {self._key}"})
return json.loads(urllib.request.urlopen(req, timeout=30).read())
except Exception: # noqa: BLE001 — transient HTTP; retry then re-raise
if a == tries - 1:
raise
time.sleep(2 * (a + 1))
raise RuntimeError("unreachable")
def _window(self) -> tuple[str, str]:
to = dt.date.today()
frm = to - dt.timedelta(days=self._lookback_days)
return frm.isoformat(), to.isoformat()
@staticmethod
def _is_crypto(symbol: str) -> bool:
if "-" not in symbol:
return False
base, _, quote = symbol.partition("-")
return quote.upper() == "USD" and base.upper() in _CRYPTO_BASES
@staticmethod
def _to_crypto_ticker(symbol: str) -> str:
"""Yahoo-style `BTC-USD` → Tiingo crypto ticker `btcusd`."""
return symbol.replace("-", "").lower()
def adj_closes(self, symbol: str) -> dict[str, float]:
frm, to = self._window()
if self._is_crypto(symbol):
return self._crypto_closes(symbol, frm, to)
return self._equity_closes(symbol, frm, to)
def _equity_closes(self, symbol: str, frm: str, to: str) -> dict[str, float]:
sym = urllib.parse.quote(symbol)
rows = self._get(_DAILY.format(base=self._base, sym=sym, frm=frm, to=to))
out: dict[str, float] = {}
for r in rows or []:
out[str(r["date"])[:10]] = float(r["adjClose"])
return out
def _crypto_closes(self, symbol: str, frm: str, to: str) -> dict[str, float]:
sym = self._to_crypto_ticker(symbol)
resp = self._get(_CRYPTO.format(base=self._base, sym=sym, frm=frm, to=to))
out: dict[str, float] = {}
for series in resp or []:
for r in series.get("priceData") or []:
out[str(r["date"])[:10]] = float(r["close"])
return out

39
src/fxhnt/adapters/data/yahoo_daily.py Normal file
View File

@@ -0,0 +1,39 @@
"""urllib DailyBarClient — Yahoo free daily adjusted closes. Retries with backoff; adjusted closes give
true total return (dividends + coupons). Mirrors foxhunt sixtyforty_paper.daily_adjclose."""
from __future__ import annotations
import datetime as dt
import json
import time
import urllib.request
from typing import Any
_BASE = "https://query1.finance.yahoo.com/v8/finance/chart/{sym}?interval=1d&range={rng}"
class YahooDailyClient:
def __init__(self, rng: str = "2y") -> None:
self._rng = rng
def _get(self, url: str, tries: int = 4) -> dict[str, Any]:
for a in range(tries):
try:
req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
data: dict[str, Any] = json.loads(urllib.request.urlopen(req, timeout=30).read())
return data
except Exception: # noqa: BLE001 — transient HTTP; retry then re-raise
if a == tries - 1:
raise
time.sleep(2 * (a + 1))
raise RuntimeError("unreachable")
def adj_closes(self, symbol: str) -> dict[str, float]:
res = self._get(_BASE.format(sym=symbol, rng=self._rng))["chart"]["result"][0]
ts = res["timestamp"]
ind = res["indicators"]
adj = ind.get("adjclose", [{}])[0].get("adjclose") or ind["quote"][0]["close"]
out: dict[str, float] = {}
for t, c in zip(ts, adj, strict=False):
if c is not None:
out[dt.datetime.fromtimestamp(t, dt.timezone.utc).strftime("%Y-%m-%d")] = float(c)
return out

91
src/fxhnt/adapters/orchestration/assets.py
View File

@@ -120,7 +120,96 @@ def combined_forward_nav(context: AssetExecutionContext) -> dict: # type: ignor
}
@asset(deps=[combined_forward_nav])
def _run_paper_tracker(context: AssetExecutionContext, name: str, build_strategy, state_file: str) -> dict: # type: ignore[type-arg,no-untyped-def]
"""Best-effort for TRANSIENT failures only: build the strategy from its live adapter, step the
tracker, persist + report state. A transient network/API error logs a warning and returns the
on-disk state without crashing the run. Genuine bugs (ValueError, KeyError, AttributeError, ...)
propagate and fail the asset loudly rather than silently flatlining the track."""
import json
import urllib.error
from fxhnt.application.forward_tracker import ForwardTracker
path = f"{_data_dir()}/{state_file}.json"
try:
st = ForwardTracker(build_strategy(), path).step()
context.log.info(f"{name}: {st.forward_days}d through {st.last_date}")
return {"forward_days": st.forward_days, "last_date": st.last_date}
except (
urllib.error.URLError,
TimeoutError,
ConnectionError,
OSError,
json.JSONDecodeError,
) as e: # transient network/IO failure must not kill the nightly run
context.log.warning(f"{name} step failed (state unchanged): {e}")
return {"forward_days": 0, "last_date": None}
@asset
def sixtyforty_nav(context: AssetExecutionContext) -> dict: # type: ignore[type-arg]
"""Paper-forward 60/40 baseline (Tiingo native total-return adjClose, 33yr history)."""
from fxhnt.adapters.data.tiingo_daily import TiingoDailyClient
from fxhnt.application.paper_strategies import SixtyFortyStrategy
return _run_paper_tracker(
context, "sixtyforty_nav", lambda: SixtyFortyStrategy(TiingoDailyClient()), "sixtyforty_state"
)
@asset
def multistrat_nav(context: AssetExecutionContext) -> dict: # type: ignore[type-arg]
"""Paper-forward multi-strategy book (Tiingo native total-return adjClose; BTC-USD via crypto endpoint)."""
from fxhnt.adapters.data.tiingo_daily import TiingoDailyClient
from fxhnt.application.paper_strategies import MultiStratStrategy
return _run_paper_tracker(
context, "multistrat_nav", lambda: MultiStratStrategy(TiingoDailyClient()), "multistrat_state"
)
@asset
def gd_nav(context: AssetExecutionContext) -> dict: # type: ignore[type-arg]
"""Paper-forward growth-discipline book (Tiingo native total-return adjClose; BTC-USD via crypto endpoint)."""
from fxhnt.adapters.data.tiingo_daily import TiingoDailyClient
from fxhnt.application.paper_strategies import GrowthDisciplineStrategy
return _run_paper_tracker(
context, "gd_nav", lambda: GrowthDisciplineStrategy(TiingoDailyClient()), "gd_paper_state"
)
@asset
def funding_nav(context: AssetExecutionContext) -> dict: # type: ignore[type-arg]
"""Paper-forward crypto funding harvest (Binance public futures API, no key)."""
from fxhnt.adapters.data.binance_funding import BinanceFundingClient
from fxhnt.application.paper_strategies import FundingCarryStrategy
return _run_paper_tracker(
context, "funding_nav", lambda: FundingCarryStrategy(BinanceFundingClient()), "funding_paper_state"
)
@asset
def crossvenue_nav(context: AssetExecutionContext) -> dict: # type: ignore[type-arg]
"""Paper-forward cross-venue funding arb (Binance/Bybit/Hyperliquid public APIs)."""
from fxhnt.adapters.data.cross_venue import CrossVenueFundingClient
from fxhnt.application.paper_strategies import CrossVenueStrategy
return _run_paper_tracker(
context, "crossvenue_nav", lambda: CrossVenueStrategy(CrossVenueFundingClient()), "crossvenue_state"
)
@asset
def poc_nav(context: AssetExecutionContext) -> dict: # type: ignore[type-arg]
"""Paper-forward surfer PoC (crypto residual-momentum + tail-managed VRP; fetches Binance + reads crypto_pit)."""
from fxhnt.application.paper_strategies import MomentumVrpStrategy
return _run_paper_tracker(context, "poc_nav", lambda: MomentumVrpStrategy(), "poc_state")
@asset(deps=[combined_forward_nav, sixtyforty_nav, multistrat_nav, gd_nav, funding_nav, crossvenue_nav, poc_nav])
def cockpit_forward(context: AssetExecutionContext) -> None:
"""Normalize tracker state files and upsert rows + summary into the operational cockpit DB."""
from fxhnt.application.forward_ingest import ingest_forward_state

22
src/fxhnt/adapters/orchestration/definitions.py
View File

@@ -1,19 +1,29 @@
"""Dagster Definitions for the B0 lifecycle graph — the 4 combined-book assets + a daily schedule that
materializes the track (replaces the fxhnt-forward cron, same 23:30 UTC slot)."""
from __future__ import annotations
materializes the track (replaces the fxhnt-forward cron, same 23:30 UTC slot).
NOTE: no `from __future__ import annotations` — Dagster 1.12 runtime type-hint inspection."""
from dagster import DefaultScheduleStatus, Definitions, ScheduleDefinition, define_asset_job
from fxhnt.adapters.orchestration.assets import (
cockpit_forward,
combined_forward_nav,
crossvenue_nav,
crypto_bars,
funding_nav,
futures_bars,
gd_nav,
multistrat_nav,
poc_nav,
sixtyforty_nav,
)
combined_book_job = define_asset_job(
name="combined_book_forward_job",
selection=[crypto_bars, futures_bars, combined_forward_nav, cockpit_forward],
selection=[
crypto_bars, futures_bars, combined_forward_nav,
sixtyforty_nav, multistrat_nav, gd_nav, funding_nav, crossvenue_nav, poc_nav,
cockpit_forward,
],
)
daily_combined_book_schedule = ScheduleDefinition(
@@ -25,7 +35,11 @@ daily_combined_book_schedule = ScheduleDefinition(
)
defs = Definitions(
assets=[crypto_bars, futures_bars, combined_forward_nav, cockpit_forward],
assets=[
crypto_bars, futures_bars, combined_forward_nav,
sixtyforty_nav, multistrat_nav, gd_nav, funding_nav, crossvenue_nav, poc_nav,
cockpit_forward,
],
jobs=[combined_book_job],
schedules=[daily_combined_book_schedule],
)

82
src/fxhnt/application/forward_tracker.py
View File

@@ -1,15 +1,17 @@
"""Forward-validation tracker for the combined book — the only ground truth before capital.
"""Strategy-agnostic forward-validation tracker — the only ground truth before capital.
Freezes the strategy at INCEPTION (first run) and, on each subsequent run, books only the realized returns
of days STRICTLY AFTER inception — a genuine out-of-sample-in-time paper track (no capital risked). Run
daily on a cron alongside the data fetch; the forward NAV + forward Sharpe accumulate honestly. The
in-sample backtest Sharpe (~1.4) means nothing until the forward record confirms it.
`ForwardTracker` freezes any `ForwardStrategy` port at INCEPTION and thereafter books only realized
returns of days STRICTLY AFTER it — a genuine out-of-sample-in-time paper track (no capital risked).
The combined book is one (recomputable) strategy on it; in-sample backtest Sharpe means nothing until
the forward record confirms it.
"""
from __future__ import annotations
import datetime as _dt
import json
import os
from dataclasses import dataclass
from typing import Any, Protocol
import numpy as np
@@ -30,9 +32,23 @@ class ForwardStatus:
booked_today: int
class CombinedBookForwardTracker:
def __init__(self, book: CombinedBook, state_path: str) -> None:
self._book = book
class ForwardStrategy(Protocol):
def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
"""Return candidate (ISO-date, fractional-return) rows the strategy knows now, plus its updated
carry-state. The tracker books only rows strictly after last_date and persists `extra` opaquely."""
...
def _today_iso() -> str:
return _dt.datetime.now(_dt.timezone.utc).strftime("%Y-%m-%d")
class ForwardTracker:
"""Strategy-agnostic forward-validation tracker. Freezes inception on first run (books nothing — no
backfill), then books only days strictly after last_date. Owns the days-list state file."""
def __init__(self, strategy: ForwardStrategy, state_path: str) -> None:
self._strategy = strategy
self._path = state_path
def _load(self) -> dict | None:
@@ -47,21 +63,25 @@ class CombinedBookForwardTracker:
json.dump(state, f, indent=2)
def step(self) -> ForwardStatus:
_, dates, combo = self._book.build() # causal sleeve combination, vol-targeted
series = {d: float(r) for d, r in zip(dates, combo) if np.isfinite(r)}
latest = dates[-1]
state = self._load()
if state is None: # first run: freeze inception, book nothing yet
state = {"inception": latest, "last_date": latest, "nav": 1.0, "days": []}
loaded = self._load()
prev_last = None if loaded is None else (loaded["last_date"] or None)
prev_extra = {} if loaded is None else loaded.get("extra", {})
rows, new_extra = self._strategy.advance(prev_last, prev_extra)
rows = sorted(rows, key=lambda x: x[0])
booked = 0
for d in sorted(series):
if d > state["last_date"]: # genuine forward: only days after the last booked
state["nav"] *= (1.0 + series[d])
state["days"].append({"date": d, "ret": series[d], "nav": state["nav"]})
state["last_date"] = d
booked += 1
if loaded is None:
latest = rows[-1][0] if rows else _today_iso()
state: dict[str, Any] = {"inception": latest, "last_date": latest, "nav": 1.0, "days": [], "extra": new_extra}
else:
state = loaded
for d, r in rows:
if d > state["last_date"]:
state["nav"] *= (1.0 + r)
state["days"].append({"date": d, "ret": r, "nav": state["nav"]})
state["last_date"] = d
booked += 1
state["extra"] = new_extra
self._save(state)
rets = np.array([row["ret"] for row in state["days"]])
@@ -70,5 +90,21 @@ class CombinedBookForwardTracker:
mdd = float((nav / np.maximum.accumulate(nav) - 1.0).min()) if len(nav) else 0.0
return ForwardStatus(
inception=state["inception"], last_date=state["last_date"], forward_days=len(state["days"]),
forward_return_pct=100.0 * (state["nav"] - 1.0), forward_sharpe=sharpe, forward_max_drawdown=mdd,
booked_today=booked)
forward_return_pct=100.0 * (state["nav"] - 1.0), forward_sharpe=sharpe,
forward_max_drawdown=mdd, booked_today=booked)
class CombinedBookStrategy:
"""Recomputable-series ForwardStrategy over a CombinedBook."""
def __init__(self, book: CombinedBook) -> None:
self._book = book
def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
_, dates, combo = self._book.build()
rows = [(d, float(r)) for d, r in zip(dates, combo) if np.isfinite(r)]
return rows, extra
def CombinedBookForwardTracker(book: CombinedBook, state_path: str) -> ForwardTracker: # noqa: N802
"""Back-compat factory: the combined book is just a recomputable ForwardStrategy."""
return ForwardTracker(CombinedBookStrategy(book), state_path)

149
src/fxhnt/application/paper_strategies.py Normal file
View File

@@ -0,0 +1,149 @@
"""The paper-forward ForwardStrategy services: each composes a market-data port + a pure domain module
and yields candidate (date, ret) rows for the generic ForwardTracker. Recomputable strategies return the
full series each run (the tracker books only the new tail); live-booking strategies book today + carry
positions in `extra`."""
from __future__ import annotations
import os
from collections.abc import Callable
from typing import Any
from fxhnt.application.forward_tracker import _today_iso
from fxhnt.domain.strategies import (
cross_venue,
funding_carry,
growth_discipline,
multistrat,
sixtyforty,
)
from fxhnt.ports.market_data import (
BinanceFundingClient,
CrossVenueFundingClient,
DailyBarClient,
)
_SIXTYFORTY_WEIGHTS = [("SPY", 0.6), ("IEF", 0.4)]
class SixtyFortyStrategy:
def __init__(self, bars: DailyBarClient) -> None:
self._bars = bars
def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
closes = {s: self._bars.adj_closes(s) for s, _ in _SIXTYFORTY_WEIGHTS}
return sixtyforty.daily_returns(closes, _SIXTYFORTY_WEIGHTS), extra
class MultiStratStrategy:
def __init__(self, bars: DailyBarClient) -> None:
self._bars = bars
def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
closes = {s: self._bars.adj_closes(s) for s in multistrat.INSTRUMENTS}
return multistrat.book_series(closes), extra
class GrowthDisciplineStrategy:
def __init__(self, bars: DailyBarClient) -> None:
self._bars = bars
def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
w = float(os.environ.get("GD_EQUITY_WEIGHT", "0.70"))
qqq = self._bars.adj_closes("QQQ")
closes = {s: self._bars.adj_closes(s) for s in multistrat.INSTRUMENTS}
return growth_discipline.daily_returns(qqq, closes, w), extra
class FundingCarryStrategy:
"""Live-booking delta-neutral funding-harvest strategy (faithful port of crypto_funding_paper.py).
Each day it fetches the universe's funding + volume, requalifies (hysteresis), books today's realized
carry on the PRIOR positions net of turnover cost, then rebalances to a fresh equal-weight book stored
in `extra["positions"]`. On the FIRST run the prior book is empty so realized/cost are 0 and the tracker
freezes (books nothing) — it just seeds initial positions, exactly like cmd_run on a fresh state."""
def __init__(self, client: BinanceFundingClient, clock: Callable[[], str] = _today_iso) -> None:
self._client = client
self._clock = clock
def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
prev: dict[str, float] = extra.get("positions", {})
liq, tf30, last24 = self._client.scan(set(prev))
q = funding_carry.qualify(liq, tf30, prev)
n = len(q)
newpos = {c: 1.0 / n for c in q} if n else {}
net = funding_carry.book_return(prev, last24, newpos)
return [(self._clock(), net)], {"positions": newpos}
class MomentumVrpStrategy:
"""Live-booking surfer PoC: crypto cross-sectional residual-momentum book + tail-managed VRP sleeve
(faithful port of foxhunt scripts/surfer/surfer_poc.py `paper`). Each run fetches the live Binance perp
panel, marks the prior two-sleeve book to today's prices + funding, scales the VRP sleeve to the
momentum-vol risk budget (calibrated once from the cached crypto_pit history), books the combined return
for today, then rebalances on the weekly boundary — all carried in `extra`. On the FIRST run the prior
book is empty so no mark happens (comb_ret 0, today == frozen inception) and the tracker freezes, just
seeding positions/prices/sleeve-equities. `today` comes from the panel's latest day (`int(days[-1])`),
not the wall clock, exactly like the original."""
_EXTRA_KEYS = ("inception", "last_mark_day", "positions", "prices", "equity", "log",
"vrp_equity", "combined_equity", "vrp_mu", "vrp_vu")
def __init__(self, cfg: dict[str, Any] | None = None) -> None:
# import here so torch/vendored surfer is only required when this strategy is actually used.
from fxhnt.vendor.surfer import surfer_poc
self._poc = surfer_poc
self._cfg = cfg if cfg is not None else surfer_poc.CFG
def _seed_state(self, extra: dict[str, Any]) -> dict[str, Any]:
"""Reconstruct surfer_poc's working state dict from the tracker's opaque `extra` (or a fresh state)."""
st: dict[str, Any] = dict(inception=None, last_mark_day=None, positions={}, prices={},
equity=1.0, log=[])
for k in self._EXTRA_KEYS:
if k in extra:
st[k] = extra[k]
return st
@staticmethod
def _iso(epoch_day: int) -> str:
import datetime as _dt
return (_dt.datetime(1970, 1, 1, tzinfo=_dt.timezone.utc)
+ _dt.timedelta(days=int(epoch_day))).strftime("%Y-%m-%d")
def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
st = self._seed_state(extra)
comb_ret, today_epoch, new_st, _info = self._poc.paper_step(self._cfg, st)
iso_today = self._iso(today_epoch)
updated = {k: new_st[k] for k in self._EXTRA_KEYS if k in new_st}
return [(iso_today, float(comb_ret))], updated
class CrossVenueStrategy:
"""Live-booking price-neutral cross-venue funding-arb strategy (faithful port of
cross_venue_funding.py). Each day it fetches per-venue funding + volume, computes per-coin spreads
(max-min daily funding across liquid, non-distress venues), requalifies via ENTRY/EXIT hysteresis +
TOPK, books today's realized carry on the PRIOR positions net of turnover cost, then rebalances to a
fresh equal-weight book stored in `extra["positions"]`. On the FIRST run the prior book is empty so
realized/cost are 0 and the tracker freezes (books nothing) — it just seeds initial positions, exactly
like cmd_run on a fresh state."""
def __init__(self, client: CrossVenueFundingClient, clock: Callable[[], str] = _today_iso) -> None:
self._client = client
self._clock = clock
def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
prev: dict[str, float] = extra.get("positions", {})
funding, volume = self._client.snapshot()
rows = cross_venue.compute_spreads(funding, volume)
cur = {r["coin"]: r["spread"] for r in rows}
held = cross_venue.select(cur, prev)
n = len(held)
newpos = {c: 1.0 / n for c in held} if n else {}
net = cross_venue.book_return(prev, cur, newpos)
return [(self._clock(), net)], {"positions": newpos}

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"""Pure cross-venue funding-arb math — faithful 1:1 port of foxhunt
scripts/surfer/cross_venue_funding.py (spreads + cmd_run booking). No I/O.
Price-neutral: for a coin liquid on >=2 venues, SHORT the highest-funding venue perp + LONG the
lowest-funding venue perp and collect the funding DIFFERENCE (max-min). Each venue's funding is already
normalized to a DAILY rate by the adapter (Binance ×24/interval, Bybit ×3, Hyperliquid ×24).
Constants match the original exactly (lines 25-31):
* LIQ = 10e6 require > $10M/day on BOTH legs (only venues passing this enter the spread)
* MAXF = 0.005 exclude any leg with |daily funding| > 50bp/day (distress/artifact, un-tradeable)
* TOPK = 10 book at most the top-K spreads
* COST_RT = 0.0010 ~10bp round-trip; net books realized − turnover * (COST_RT/2)
* HURDLE = 0.0005 (display-only in the original scan; not used in select/book)
* ENTRY = 0.0010 hysteresis: only OPEN a fresh pair whose spread > 10bp/day
* EXIT = 0.0005 hysteresis: HOLD a held pair until its spread decays below 5bp/day
Original spread (spreads(), lines 90-96): per coin, keep only venues whose 24h volume > LIQ AND whose
|daily funding| <= MAXF; if >=2 such venues remain, spread = max(f) - min(f), short = argmax, long = argmin.
Original select (cmd_run, lines 124-135):
cur = {coin: spread}
held = [c for c in prev if cur.get(c, 0.0) > EXIT] # keep decaying winners
for r in sorted(rows with spread > ENTRY, by -spread): # open strong fresh
if len(held) >= TOPK: break
if r.coin not in held: held.append(r.coin)
newpos = {c: 1/len(held)}
Original booking (cmd_run, lines 126, 136-137):
realized = sum(w * cur.get(c, 0.0) for c, w in prev.items()) # carry on prior book at TODAY's spread
turn = sum(|newpos[c] - prev[c]| over union)
net = realized - turn * (COST_RT / 2)
"""
from __future__ import annotations
from typing import Any
LIQ = 10e6
MAXF = 0.005
TOPK = 10
COST_RT = 0.0010
HURDLE = 0.0005
ENTRY = 0.0010
EXIT = 0.0005
def compute_spreads(
funding_by_venue: dict[str, dict[str, float]],
volume_by_venue: dict[str, dict[str, float]],
liq: float = LIQ,
maxf: float = MAXF,
) -> list[dict[str, Any]]:
"""Per-coin cross-venue spread rows, sorted by spread descending (faithful to spreads(), lines 88-98).
For each coin, keep only venues where 24h volume > `liq` and |daily funding| <= `maxf`; require >=2
such venues; emit {coin, spread = max-min funding, short = argmax venue, long = argmin venue, f}."""
rows: list[dict[str, Any]] = []
for c, venue_f in funding_by_venue.items():
venue_v = volume_by_venue.get(c, {})
pts = {
nm: f
for nm, f in venue_f.items()
if venue_v.get(nm, 0.0) > liq and abs(f) <= maxf
}
if len(pts) < 2:
continue
hi = max(pts, key=lambda k: pts[k])
lo = min(pts, key=lambda k: pts[k])
rows.append({"coin": c, "spread": pts[hi] - pts[lo], "short": hi, "long": lo, "f": pts})
rows.sort(key=lambda r: -r["spread"])
return rows
def select(
cur: dict[str, float],
prev: dict[str, float],
entry: float = ENTRY,
exit_: float = EXIT,
topk: int = TOPK,
) -> list[str]:
"""Coins to hold today via ENTRY/EXIT hysteresis + TOPK cap (faithful to cmd_run lines 128-134).
`cur` is {coin: today's spread}. Keep held coins whose spread is still > `exit_`; then open fresh
coins whose spread > `entry` (sorted by spread desc) until `topk` total are held."""
held = [c for c in prev if cur.get(c, 0.0) > exit_]
fresh = sorted(
(c for c, s in cur.items() if s > entry),
key=lambda c: -cur[c],
)
for c in fresh:
if len(held) >= topk:
break
if c not in held:
held.append(c)
return held
def book_return(
prev: dict[str, float],
cur: dict[str, float],
newpos: dict[str, float],
cost_rt: float = COST_RT,
) -> float:
"""Net realized carry: equal-weight realized spread on the PRIOR book at TODAY's spread `cur`,
minus the round-trip cost on turnover between the prior and new books. Faithful to cmd_run
(lines 126, 136-137): `net = realized - turn * (COST_RT / 2)`."""
realized = sum(w * cur.get(c, 0.0) for c, w in prev.items())
turnover = sum(
abs(newpos.get(c, 0.0) - prev.get(c, 0.0))
for c in set(newpos) | set(prev)
)
return realized - turnover * (cost_rt / 2)

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"""Pure delta-neutral funding-harvest math — faithful 1:1 port of foxhunt
scripts/surfer/crypto_funding_paper.py (qualify + cmd_run booking). No I/O.
Constants and logic match the original exactly:
* HURDLE = 5e-4 open a position when trailing-30d mean daily funding (tf30) > 5 bp/day
* EXIT_HURDLE = 3e-4 hysteresis: keep a HELD coin until its tf30 falls below 3 bp/day
* LIQ_USD = 5e6 only coins with > $5M/day quote volume are in the liquid universe (in `liq`)
* COST_RT = 1e-3 10 bp round-trip; the run books realized − turnover * (COST_RT/2)
Original `qualify(liq, tf30, prev)` (lines 95-101): loops over the *liquid* universe `liq`
(already volume-filtered + crypto-native + spot-hedgeable in `universe()`/`scan()`), keeping `c` when
its TRAILING-30d-MEAN funding `tf30[c] > HURDLE` or (`c in prev and tf30[c] > EXIT_HURDLE`). Returns
`{c: tf30[c]}`. Held coins outside the liquid universe are NOT kept (the loop is over `liq` only).
Original booking (cmd_run lines 136-141):
realized = sum(w * last24[c] for c,w in prev.items()) # equal-weight carry on prior book (TODAY's rate)
turnover = sum(|newpos[c] - prev[c]| over union)
net = realized - turnover * (COST_RT / 2)
"""
from __future__ import annotations
HURDLE = 5e-4
EXIT_HURDLE = 3e-4
LIQ_USD = 5e6
COST_RT = 1e-3
def qualify(
liq: dict[str, float],
tf30: dict[str, float],
prev: dict[str, float],
hurdle: float = HURDLE,
exit_hurdle: float = EXIT_HURDLE,
) -> dict[str, float]:
"""Coins to hold today, qualified on the TRAILING-30d-MEAN funding `tf30` (NOT today's rate).
Liquidity/native/hedgeability are already enforced by `liq` (the keys of `liq`). Then hysteresis:
open new coins whose tf30 > `hurdle`, keep held coins whose tf30 is still > `exit_hurdle`.
Returns `{c: tf30[c]}` (faithful to original lines 95-101)."""
q: dict[str, float] = {}
for c in liq:
t = tf30.get(c)
if t is not None and (t > hurdle or (c in prev and t > exit_hurdle)):
q[c] = t
return q
def book_return(
prev: dict[str, float],
last24: dict[str, float],
newpos: dict[str, float],
cost_rt: float = COST_RT,
) -> float:
"""Net realized carry: equal-weight realized funding on the PRIOR book at TODAY's booking rate
`last24`, minus the round-trip cost on turnover between the prior and new books. Faithful to
cmd_run (lines 136-141): `net = realized - turnover * (COST_RT / 2)`."""
realized = sum(w * last24.get(c, 0.0) for c, w in prev.items())
turnover = sum(
abs(newpos.get(c, 0.0) - prev.get(c, 0.0))
for c in set(newpos) | set(prev)
)
return realized - turnover * (cost_rt / 2)

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"""Growth-discipline blend: w·QQQ + (1−w)·multistrat book, on the intersection of their dates. Pure."""
from __future__ import annotations
from fxhnt.domain.strategies import multistrat
def daily_returns(qqq: dict[str, float], multistrat_closes: dict[str, dict[str, float]],
w_equity: float = 0.70) -> list[tuple[str, float]]:
ms = dict(multistrat.book_series(multistrat_closes))
qdates = sorted(qqq)
# Intentional alignment choice vs foxhunt original: foxhunt samples QQQ on the book's date grid;
# this port computes QQQ returns on QQQ's own consecutive dates then intersects with the book dates.
# Identical on a clean shared trading calendar; on a data hole this drops the gap day's move rather
# than bridging it as a mislabelled 1-day return.
qret = {qdates[i]: qqq[qdates[i]] / qqq[qdates[i - 1]] - 1.0 for i in range(1, len(qdates))}
common = sorted(set(ms) & set(qret))
return [(d, w_equity * qret[d] + (1.0 - w_equity) * ms[d]) for d in common]

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"""Pure adaptive multi-strat book — faithful port of foxhunt scripts/surfer/multistrat_paper.py
(build/volnorm/trust/book_series). 6 streams (SPY/IEF/GLD/PDBC/DBMF + BTC-USD): vol-normalize each
stream -> edge-decay trust theta -> trust-weighted combination -> adaptive risk layer (EMA vol,
Kelly-floor, continuous self-recovering drawdown de-lever, z-score correlation de-risk, leverage-floor),
UNLEVERED (MAXLEV 1.0). numpy internal; no I/O; no network."""
from __future__ import annotations
import datetime
import math
import numpy as np
import numpy.typing as npt
# The exact instrument list (Yahoo tickers) the original uses, in order.
INSTRUMENTS: list[str] = ["SPY", "IEF", "GLD", "PDBC", "DBMF", "BTC-USD"]
TARGET_VOL, MAXLEV, KELLY_FLOOR, LEV_FLOOR = 0.10, 1.0, 0.5, 0.3
DD_DEADBAND, DD_SENS, DD_FLOOR = 0.05, 3.0, 0.40
def _build(closes: dict[str, dict[str, float]]) -> tuple[list[str], npt.NDArray[np.float64]]:
"""Port of build(): intersect dates across all streams, drop today's incomplete bar, return
(dates, return-matrix R[T, S]) where R[0, :] = 0."""
dates = sorted(set.intersection(*[set(closes[s]) for s in INSTRUMENTS]))
# drop today's INCOMPLETE intraday bar if present — daily-CLOSE strategy.
today = datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%d")
if dates and dates[-1] == today:
dates = dates[:-1]
R = np.zeros((len(dates), len(INSTRUMENTS)))
for j, sym in enumerate(INSTRUMENTS):
s = np.array([closes[sym][d] for d in dates])
if len(s) > 1:
R[1:, j] = s[1:] / s[:-1] - 1
return dates, R
def _volnorm(col: npt.NDArray[np.float64], tv: float = TARGET_VOL, win: int = 63) -> npt.NDArray[np.float64]:
out = np.zeros_like(col)
for t in range(win, len(col)):
rv = col[t - win:t].std() * math.sqrt(252)
out[t] = col[t] * min(5.0, tv / (rv + 1e-9))
return out
def _trust(col: npt.NDArray[np.float64], win: int = 126, a: float = 0.06) -> npt.NDArray[np.float64]:
th = 1.0
out = np.ones_like(col)
for t in range(win, len(col)):
seg = col[t - win:t]
sr = seg.mean() / (seg.std() + 1e-9) * math.sqrt(252)
th = (1 - a) * th + a * float(np.clip((sr + 0.5) / 1.0, 0.1, 1.0))
out[t] = th
return out
def book_series(closes: dict[str, dict[str, float]]) -> list[tuple[str, float]]:
"""Faithful port of book_series(R): produce the unlevered daily book return for each booked day.
Skips the warmup window exactly as the original (book[t+1] populated only for t in [63, T-1))."""
dates, R = _build(closes)
S = R.shape[1]
M = np.column_stack([_volnorm(R[:, j]) for j in range(S)])
TH = np.column_stack([_trust(M[:, j]) for j in range(S)])
tw = TH / np.maximum(TH.sum(1, keepdims=True), 1e-9)
combo = np.nansum(tw * M, axis=1)
T = len(combo)
book = np.zeros(T)
eq = 1.0
peak = 1.0
emu = float(np.nanmean(combo[:63])) if T >= 1 else 0.0
evar = (float(np.nanvar(combo[:63])) if T >= 1 else 0.0) + 1e-12
chist: list[float] = []
for t in range(63, T - 1):
x = combo[t]
emu = 0.97 * emu + 0.03 * x
evar = 0.97 * evar + 0.03 * (x - emu) ** 2
L = min(MAXLEV, TARGET_VOL / (math.sqrt(max(evar, 1e-12) * 252) + 1e-9))
L = min(L, max(KELLY_FLOOR, (emu * 252) / (evar * 252 + 1e-9)))
dd = eq / peak - 1.0
L *= float(np.clip(1 - DD_SENS * max(0.0, -dd - DD_DEADBAND), DD_FLOOR, 1.0))
sub = np.nan_to_num(M[t - 63:t])
vmask = sub.std(0) > 1e-12 # only streams that vary (avoid /0 in corrcoef -> NaN)
if int(vmask.sum()) >= 2:
k = int(vmask.sum())
cm = np.corrcoef(sub[:, vmask].T)
ac = float((np.nansum(cm) - k) / (k * k - k))
else:
ac = 0.0
chist.append(ac)
if len(chist) > 60:
h = np.array(chist[-120:])
z = (ac - h.mean()) / (h.std() + 1e-9)
L *= float(np.clip(1 - 0.2 * max(0.0, z), 0.5, 1.0))
L = float(np.clip(L, LEV_FLOOR, MAXLEV))
book[t + 1] = L * combo[t + 1]
eq *= (1 + book[t + 1])
peak = max(peak, eq)
# emit only the booked tail (warmup days have book==0 and no date booked by the original loop):
# booked indices are t+1 for t in [63, T-1) -> [64, T-1]
out: list[tuple[str, float]] = []
for i in range(64, T):
out.append((dates[i], float(book[i])))
return out

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"""Pure 60/40 daily-rebalanced return: for each consecutive date pair, the weighted mean of per-asset
returns on adjusted closes. No I/O."""
from __future__ import annotations
def daily_returns(closes: dict[str, dict[str, float]], weights: list[tuple[str, float]]) -> list[tuple[str, float]]:
syms = [s for s, _ in weights]
dates = sorted(set.intersection(*[set(closes[s]) for s in syms]))
out: list[tuple[str, float]] = []
for i in range(1, len(dates)):
d, dprev = dates[i], dates[i - 1]
r = sum(w * (closes[s][d] / closes[s][dprev] - 1.0) for s, w in weights)
out.append((d, r))
return out

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# src/fxhnt/ports/market_data.py
"""Market-data client ports for the paper-forward strategies. Each has a urllib live adapter
(adapters/data/*) and a deterministic fake in tests. No network in the domain or in tests."""
from __future__ import annotations
from typing import Protocol
class DailyBarClient(Protocol):
def adj_closes(self, symbol: str) -> dict[str, float]:
"""{ 'YYYY-MM-DD': adjusted_close } for a daily-bar history window."""
...
class BinanceFundingClient(Protocol):
def scan(self, prev: set[str]) -> tuple[dict[str, float], dict[str, float], dict[str, float]]:
"""(liq, tf30, last24) for the union of the hedgeable liquid universe and prev positions.
liq: {symbol: 24h_quote_volume_usd} for crypto-native, spot-hedgeable USDT perps with vol>LIQ_USD.
tf30: {symbol: 30d-mean funding × INTERVALS_PER_DAY} (daily rate; only symbols with >=30 history).
last24: {symbol: sum of the most recent INTERVALS_PER_DAY funding intervals} (today's booking rate)."""
...
class CrossVenueFundingClient(Protocol):
def snapshot(self) -> tuple[dict[str, dict[str, float]], dict[str, dict[str, float]]]:
"""One fetch per run → (funding_by_venue, volume_by_venue), both from the same snapshot.
funding_by_venue: { coin: { venue: daily_funding_rate } } across the supported venues.
volume_by_venue: { coin: { venue: 24h_quote_volume_usd } }."""
...
class CryptoPerpBarClient(Protocol):
def panel(self) -> tuple[list[str], list[int], list[list[float]], list[list[float]], list[float]]:
"""(symbols, epoch_days, close[d][s], open[d][s], funding_now[s]) for the top-liquid perps."""
...
class VolIndexClient(Protocol):
def dvol(self, currency: str) -> dict[int, float]:
"""{ epoch_day: implied-vol-index } for BTC/ETH (Deribit DVOL)."""
...

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"""Vendored third-party / sibling-repo code, copied verbatim with only import/path edits.
These modules are NOT maintained here; they are faithful copies of their upstream source so the
behaviour is identical. See each subpackage's __init__ for provenance and the exact edits made.
"""

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"""Vendored surfer PoC (crypto cross-sectional momentum + tail-managed VRP sleeve).
Provenance: copied verbatim from foxhunt `scripts/surfer/{signal_sweep,pit_sweep,surfer_poc}.py`.
ONLY edits applied (no algorithm / math / constant changes):
* `sys.path.insert(...)` + bare sibling imports rewritten as package-relative
(`from fxhnt.vendor.surfer.signal_sweep import ...`, `from fxhnt.vendor.surfer import pit_sweep`).
* data root made configurable via `$FXHNT_SURFER_DATA_DIR` (default `/data/surfer`):
`pit_sweep.load()` reads `$FXHNT_SURFER_DATA_DIR/crypto_pit/*.npz`; `surfer_poc` resolves
`STATE` and the dvol cache under `$FXHNT_SURFER_DATA_DIR`.
* `surfer_poc.paper_step(cfg, state)` added: a value-returning refactor of `paper()` for the
fxhnt ForwardTracker (same mark / weekly-rebalance / VRP-scaling math, no file IO / printing).
torch is imported at module level (used in `signal_sweep.sharpe_t`/`validate` and `surfer_poc.backtest`)
and is kept — this is a faithful port, not a numpy reimplementation.
"""

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#!/usr/bin/env python3
"""Point-in-time survivorship test for crypto cross-sectional momentum.
Universe is rebuilt EACH DAY: among coins with active trailing-30d dollar-volume (>0),
take the top-K by that volume. Dead coins (LUNA, SRM, ...) are IN the cross-section while
they trade and DROP OUT (after carrying their crash) when volume dies. No lookahead:
volume & momentum use only past data; weights apply to next-day return. If momentum's
edge survives here — especially 2022 with LUNA included — it is real beyond the bootstrap proxy.
"""
import glob
import math
import os
import numpy as np
from fxhnt.vendor.surfer.signal_sweep import xs_weights, pnl_w, validate, sharpe_t # noqa: E402,F401
import torch # noqa: E402
DEV = "cuda" if torch.cuda.is_available() else "cpu"
def load():
syms, data = [], {}
_base = os.environ.get("FXHNT_SURFER_DATA_DIR", "/data/surfer") # crypto_pit lives at $FXHNT_SURFER_DATA_DIR/crypto_pit
for p in sorted(glob.glob(os.path.join(_base, "crypto_pit/*.npz"))):
d = np.load(p); s = p.split("/")[-1][:-4]
data[s] = (d["day"].astype(np.int64), d["close"].astype(float), d["qvol"].astype(float), d["funding"].astype(float))
syms.append(s)
syms = sorted(syms)
days = np.array(sorted(set().union(*[set(data[s][0].tolist()) for s in syms])))
di = {int(v): i for i, v in enumerate(days.tolist())}
T, N = len(days), len(syms)
close = np.full((T, N), np.nan); qv = np.full((T, N), np.nan); fund = np.full((T, N), np.nan)
for j, s in enumerate(syms):
dd, cc, vv, ff = data[s]
for k in range(len(dd)):
r = di[int(dd[k])]; close[r, j] = cc[k]; qv[r, j] = vv[k]; fund[r, j] = ff[k]
return syms, days, close, qv, fund
def trailing(lc, L):
out = np.full_like(lc, np.nan); out[L:] = lc[L:] - lc[:-L]; return out
def main():
syms, days, close, qv, fund = load()
T, N = close.shape
lc = np.log(close)
R = np.zeros((T, N)); R[1:] = lc[1:] - lc[:-1]; R = np.where(np.isfinite(R), R, 0.0)
Reff = R - np.where(np.isfinite(fund), fund, 0.0)
qv = np.nan_to_num(qv)
dv30 = np.full((T, N), 0.0) # trailing 30d mean dollar-volume
for t in range(30, T):
dv30[t] = qv[t - 30:t].mean(axis=0)
dead = sum(1 for j in range(N) if qv[-30:, j].mean() == 0)
year = (1970 + days / 365.25).astype(int)
print(f"\n===== POINT-IN-TIME MOMENTUM — {N} coins ({dead} dead/delisted), {T}d =====")
for TOPK in [30, 50]:
# daily universe = top-K by trailing dollar-vol among actively-trading coins
univ = np.zeros((T, N), bool)
for t in range(T):
elig = np.where((dv30[t] > 0) & np.isfinite(close[t]))[0]
if len(elig) == 0:
continue
k = min(TOPK, len(elig))
top = elig[np.argsort(-dv30[t, elig])[:k]]
univ[t, top] = True
print(f"\n--- TOPK={TOPK} (avg coins/day in-universe = {univ.sum(1).mean():.0f}) ---")
print(f"{'lookback':>9} {'full':>6} {'IS':>6} {'OOS':>6} {'CPCVmed':>8} {'DSR':>5} | per-year (2020..2026)")
for L in [10, 20, 30]:
sig = trailing(lc, L).copy()
sig[~univ] = np.nan # restrict signal to PIT universe
w = xs_weights(sig)
pnl = pnl_w(w, Reff, cost_bp=10)
v = validate(pnl, days, 6)
py = []
for y in range(2020, 2027):
m = year[1:] == y
py.append(sharpe_t(torch.tensor(pnl[m], device=DEV, dtype=torch.float64)) if m.sum() > 30 else float("nan"))
pys = " ".join(f"{p:>+5.2f}" if not math.isnan(p) else " n/a" for p in py)
print(f"{L:>9} {v['full']:>+6.2f} {v['is_']:>+6.2f} {v['oos']:>+6.2f} {v['med']:>+8.2f} {v['dsr']:>5.2f} | {pys}")
print("\nVERDICT: if mom_20-30 stays positive (esp. 2022 with LUNA in-universe) => survivorship-robust REAL edge.")
if __name__ == "__main__":
main()

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src/fxhnt/vendor/surfer/signal_sweep.py vendored Normal file
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#!/usr/bin/env python3
"""Deflated signal sweep — a factor zoo on data in hand, judged against the FULL search.
Wisdom: breadth without multiple-testing control is the fastest way to fool yourself.
So every signal's Deflated Sharpe is deflated by N_trials = the number of signals tested.
Batch 1 (instant, free, data already downloaded): 22-futures daily panel — cross-sectional
& time-series momentum, short-term reversal, low-vol — plus structural SEASONALITY
(overnight-drift, intraday, day-of-week). Each signal → daily PnL → full/IS/OOS Sharpe,
CPCV(45-path) median+5th-pct, Deflated Sharpe. Ranked by OOS. Survivor = DSR>0.95 AND
OOS>0 AND CPCV-median>0 (consistent + multiple-testing-honest).
Validation on GPU (torch); panel/signal staging on CPU (small). Roll-zeroed close-close
returns (screening; survivors get proper roll handling in deeper validation).
"""
import glob
import math
import re
from itertools import combinations
from statistics import NormalDist
import numpy as np
import torch
ND = NormalDist()
DEV = "cuda" if torch.cuda.is_available() else "cpu"
MONTHS = "FGHJKMNQUVXZ"
SPLIT_DAY = 19723 # 2024-01-01 in days-since-epoch
def load_panel():
import databento as db
series = {}
for p in sorted(glob.glob("data/surfer/*.dbn")):
root = p.split("/")[-1][:-4]
pat = re.compile("^" + re.escape(root) + "[" + MONTHS + r"]\d{1,2}$")
try:
df = db.DBNStore.from_file(p).to_df().reset_index()
except Exception:
continue
if df.empty or "close" not in df.columns:
continue
df = df[df["close"] > 0]
df = df[df["symbol"].astype(str).str.match(pat)]
if df.empty:
continue
df["day"] = (df["ts_event"].astype("int64") // (86_400 * 10**9))
idx = df.groupby("day")["volume"].idxmax()
f = df.loc[idx, ["day", "instrument_id", "open", "close"]].sort_values("day")
series[root] = (f["day"].to_numpy(np.int64), f["instrument_id"].to_numpy(np.int64),
f["open"].to_numpy(np.float64), f["close"].to_numpy(np.float64))
roots = sorted(series)
days = np.array(sorted(set().union(*[set(series[r][0].tolist()) for r in roots])))
di = {d: i for i, d in enumerate(days.tolist())}
T, N = len(days), len(roots)
close = np.full((T, N), np.nan); op = np.full((T, N), np.nan); inst = np.full((T, N), -1, np.int64)
for j, r in enumerate(roots):
d, ii, oo, cc = series[r]
for k in range(len(d)):
row = di[int(d[k])]; close[row, j] = cc[k]; op[row, j] = oo[k]; inst[row, j] = ii[k]
weekday = (days + 3) % 7 # epoch day0 = Thursday → 0=Mon..6=Sun
return roots, days, close, op, inst, weekday
def build_returns(close, op, inst):
T, N = close.shape
lc = np.log(close)
R = np.zeros((T, N)); R[1:] = lc[1:] - lc[:-1]
same = np.zeros((T, N), bool); same[1:] = inst[1:] == inst[:-1]
R = np.where(same & np.isfinite(R), R, 0.0)
ov = np.zeros((T, N)); ov[1:] = np.log(op[1:] / close[:-1])
ov = np.where(same & np.isfinite(ov), ov, 0.0)
intr = np.log(close / op); intr = np.where(np.isfinite(intr), intr, 0.0)
return lc, R, ov, intr
def trailing(lc, L):
out = np.full_like(lc, np.nan); out[L:] = lc[L:] - lc[:-L]; return out
def xs_weights(sig): # cross-sectional z-score, market-neutral, unit gross
mu = np.nanmean(sig, axis=1, keepdims=True); sd = np.nanstd(sig, axis=1, keepdims=True)
z = np.nan_to_num((sig - mu) / np.where(sd > 0, sd, 1))
g = np.sum(np.abs(z), axis=1, keepdims=True); g[g == 0] = 1
return z / g
def pnl_w(w, R, cost_bp=2.0):
p = np.sum(w[:-1] * R[1:], axis=1)
turn = np.sum(np.abs(w[1:] - w[:-1]), axis=1)
return p - turn * (cost_bp / 1e4)
def sharpe_t(x):
x = x[torch.isfinite(x)]
if len(x) < 10 or float(x.std()) == 0:
return float("nan")
return float(x.mean() / x.std() * math.sqrt(252))
def validate(pnl, days, n_trials):
x = torch.tensor(np.asarray(pnl), device=DEV, dtype=torch.float64)
n = len(x); full = sharpe_t(x)
dd = days[-n:]
ism = torch.tensor(dd < SPLIT_DAY, device=DEV)
sis = sharpe_t(x[ism])
soos = sharpe_t(x[~ism]) if int((~ism).sum()) > 30 else float("nan")
nb, k = 10, 2
blk = [torch.arange(i * n // nb, (i + 1) * n // nb, device=DEV) for i in range(nb)]
oos = [sharpe_t(x[torch.cat([blk[b] for b in c])]) for c in combinations(range(nb), k)]
oos = np.array([o for o in oos if not math.isnan(o)])
med = float(np.median(oos)) if len(oos) else float("nan")
p5 = float(np.percentile(oos, 5)) if len(oos) else float("nan")
srd = full / math.sqrt(252)
if n_trials > 1:
sr0 = (1 / math.sqrt(n)) * ((1 - 0.5772) * ND.inv_cdf(1 - 1.0 / n_trials)
+ 0.5772 * ND.inv_cdf(1 - 1.0 / (n_trials * math.e)))
else:
sr0 = 0.0
dsr = ND.cdf((srd - sr0) * math.sqrt(n - 1)) if not math.isnan(full) else float("nan")
return dict(full=full, is_=sis, oos=soos, med=med, p5=p5, dsr=dsr)
def main():
roots, days, close, op, inst, weekday = load_panel()
lc, R, ov, intr = build_returns(close, op, inst)
T, N = close.shape
vol63 = np.full_like(lc, np.nan)
for t in range(63, T):
vol63[t] = np.nanstd(R[t - 63:t], axis=0)
invvol = 1.0 / np.where(vol63 > 0, vol63, np.nan)
sig = {}
for L in [21, 63, 126, 252]:
sig[f"XS_mom_{L}"] = pnl_w(xs_weights(trailing(lc, L)), R)
for L in [1, 5]:
sig[f"XS_rev_{L}"] = pnl_w(xs_weights(-trailing(lc, L)), R)
sig["XS_lowvol"] = pnl_w(xs_weights(-vol63), R)
for L in [21, 63, 252]:
w = np.nan_to_num(np.sign(trailing(lc, L)) * invvol)
g = np.sum(np.abs(w), axis=1, keepdims=True); g[g == 0] = 1
sig[f"TS_mom_{L}"] = pnl_w(w / g, R)
sig["SEAS_overnight"] = np.nanmean(ov[1:], axis=1) - 1.0 / 1e4 # EW long overnight, ~1bp cost
sig["SEAS_intraday"] = np.nanmean(intr[1:], axis=1) - 1.0 / 1e4 # EW long intraday
ewR = np.nanmean(R, axis=1)
for d, nm in enumerate(["Mon", "Tue", "Wed", "Thu", "Fri"]):
sig[f"SEAS_{nm}"] = np.where(weekday[1:] == d, ewR[1:], 0.0)
NT = len(sig)
rows = [(nm, validate(p, days, NT)) for nm, p in sig.items()]
rows.sort(key=lambda r: -(r[1]["oos"] if not math.isnan(r[1]["oos"]) else -9))
print(f"\n===== DEFLATED SIGNAL SWEEP — Batch 1 (22 futures daily + seasonality) =====")
print(f"device={DEV} roots={len(roots)} days={T} N_trials(deflation)={NT}")
print(f"{'signal':>16} {'full':>7} {'IS':>7} {'OOS':>7} {'CPCVmed':>8} {'CPCV5%':>8} {'DSR':>6}")
for nm, v in rows:
print(f"{nm:>16} {v['full']:>+7.2f} {v['is_']:>+7.2f} {v['oos']:>+7.2f} "
f"{v['med']:>+8.2f} {v['p5']:>+8.2f} {v['dsr']:>6.2f}")
surv = [nm for nm, v in rows if v["dsr"] > 0.95 and v["oos"] > 0 and v["med"] > 0]
print(f"\nSURVIVORS (DSR>0.95 & OOS>0 & CPCVmed>0): {surv if surv else 'NONE'}")
print("DSR is deflated by N_trials → multiple-testing-honest. Survivors get deeper validation + crypto/COT batches.")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""Surfer PoC — crypto cross-sectional momentum (the validated edge), end-to-end.
Modes:
backtest — run the strategy over the cached point-in-time history; print honest stats
(gross/net Sharpe at AUM, OOS, CPCV-median, DSR, per-year, max-DD, turnover, capacity curve)
regime — diagnostic: does any regime feature predict when momentum works? (overlay is OFF by default)
paper — forward paper-trade: fetch live data, compute today's target weights, log intended trades,
mark the paper book (price + funding), persist state. The only true out-of-sample test.
status — print the paper book + equity curve summary.
Design: ONE signal function (`compute_weights`) drives BOTH backtest and live (no backtest/live skew).
No lookahead (signal uses closed bars <= t; weights apply to t+1; rebalance keyed on calendar day%K).
Funding is P&L (longs pay, shorts earn). Realistic sqrt-impact cost. Validated edge only; regime overlay
stays a diagnostic until separately confirmed.
"""
import argparse
import json
import math
import os
import time
import urllib.request
import numpy as np
from fxhnt.vendor.surfer.signal_sweep import xs_weights, validate, sharpe_t # noqa: E402,F401
from fxhnt.vendor.surfer import pit_sweep # noqa: E402 (load() of the cached PIT universe)
import torch # noqa: E402
DEV = "cuda" if torch.cuda.is_available() else "cpu"
DAY_MS = 86_400_000
def _data_dir(): # resolved per-call so $FXHNT_SURFER_DATA_DIR is honored at runtime (default /data/surfer)
return os.environ.get("FXHNT_SURFER_DATA_DIR", "/data/surfer")
STATE = os.path.join(_data_dir(), "poc_state.json") # CLI default; paper_step is value-returning (no file IO)
CFG = dict(
lookback=20, # XS momentum horizon (days)
beta_win=60, # rolling window for market-beta (residual momentum strips this)
topk=30, # universe = top-K liquid by trailing dollar-vol (breadth/liquidity optimum)
rebal_k=7, # rebalance every 7 calendar days (weekly)
smooth_span=5, # EWMA weight-smoothing span (cuts turnover + whipsaw)
vol_win=30, # trailing vol window (for sizing / regime)
cost_aum=2e7, # AUM the slippage model is priced at ($20M)
eta=1.0, # sqrt market-impact coefficient (conservative)
dd_breaker=-0.20, # cut gross exposure if rolling drawdown worse than this
vrp_risk_frac=0.30, # VRP diversifier sleeve risk budget (small — short-vol tail)
vrp_rise_k=5, # don't sell vol when DVOL rose over last K days (tail gate)
vrp_level_win=60, # sell full size only when DVOL < trailing-win median
)
# ---------------------------------------------------------------- helpers
def roll(fn, X, L):
out = np.full_like(X, np.nan)
for t in range(L, len(X)):
out[t] = fn(X[t - L:t], axis=0)
return out
def trailing(lc, L):
out = np.full_like(lc, np.nan); out[L:] = lc[L:] - lc[:-L]; return out
def ewma_rows(W, span):
if span <= 1:
return W
a = 2.0 / (span + 1)
out = W.copy()
for t in range(1, len(W)):
out[t] = a * W[t] + (1 - a) * out[t - 1]
return out
# ---------------------------------------------------------------- SHARED SIGNAL (backtest == live)
def compute_weights(close, qvol, days, cfg):
"""Panel -> held target weights [T,N] (market-neutral, weekly, smoothed). No lookahead.
Live takes the last row; backtest uses all rows. Identical code path."""
T, N = close.shape
lc = np.log(close)
dv = roll(np.mean, np.nan_to_num(qvol), 30)
univ = np.zeros((T, N), bool)
for t in range(T):
elig = np.where((dv[t] > 0) & np.isfinite(close[t]))[0]
if len(elig):
univ[t, elig[np.argsort(-dv[t, elig])[:cfg["topk"]]]] = True
# residual (beta-stripped) momentum: strip each coin's beta to the equal-weight market,
# rank the residual trend (Phase-1: +0.72 vs +0.57 raw — cleaner base edge). No lookahead.
R = np.zeros((T, N)); R[1:] = lc[1:] - lc[:-1]; R = np.where(np.isfinite(R), R, 0.0)
mkt = np.array([R[t][univ[t]].mean() if univ[t].any() else 0.0 for t in range(T)])
Wb = cfg["beta_win"]; beta = np.zeros((T, N))
for t in range(Wb, T):
mw = mkt[t - Wb:t]; vb = mw.var() + 1e-12
beta[t] = ((R[t - Wb:t] * mw[:, None]).mean(0) - R[t - Wb:t].mean(0) * mw.mean()) / vb
rcum = np.cumsum(R - beta * mkt[:, None], axis=0)
L = cfg["lookback"]
sig = np.full((T, N), np.nan); sig[L:] = rcum[L:] - rcum[:-L]
sig[~univ] = np.nan
wt = xs_weights(sig) # daily target (market-neutral, unit gross)
wt = ewma_rows(wt, cfg["smooth_span"]) # smooth
held = wt.copy() # weekly rebalance on FIXED calendar phase (day%K==0)
K = cfg["rebal_k"] # epoch day0=Thu → rebalances every Thursday, live-consistent
last = 0
for t in range(T):
if days[t] % K == 0:
last = t
held[t] = wt[last]
return held, univ
def slippage_net(w, Reff, dv, vol, days, cfg):
"""Net daily PnL series under sqrt market-impact + liquidity-scaled spread at cfg['cost_aum']."""
AUM = cfg["cost_aum"]
turn = np.abs(w[1:] - w[:-1])
adv = np.nan_to_num(dv[1:]); sg = np.nan_to_num(vol[1:])
hs = np.clip(30.0 / np.sqrt(np.maximum(adv, 1.0) / 1e6), 1.0, 30.0) / 1e4
part = np.where(adv > 0, turn * AUM / adv, 0.0)
cost = np.sum(turn * (hs + cfg["eta"] * sg * np.sqrt(np.clip(part, 0, None))), axis=1)
return np.sum(w[:-1] * Reff[1:], axis=1) - cost
def max_drawdown(pnl):
eq = np.cumsum(np.nan_to_num(pnl)); peak = np.maximum.accumulate(eq)
return float((eq - peak).min())
# ---------------------------------------------------------------- VRP SLEEVE (tail-managed short-vol diversifier)
def _dvol(ccy, refresh):
cache = os.path.join(_data_dir(), "dvol", f"{ccy}.json")
d = {int(k): v for k, v in json.load(open(cache)).items()} if os.path.exists(cache) else {}
if refresh:
end = int(time.time() * 1000); start = end - 200 * DAY_MS
try:
rows = _get(f"https://www.deribit.com/api/v2/public/get_volatility_index_data?currency={ccy}"
f"&start_timestamp={start}&end_timestamp={end}&resolution=86400").get("result", {}).get("data", [])
for r in rows:
d[int(r[0]) // DAY_MS] = float(r[4])
os.makedirs(os.path.dirname(cache), exist_ok=True)
json.dump({str(k): v for k, v in d.items()}, open(cache, "w"))
except Exception:
pass
return d
def vrp_ccy(iv, close, cfg):
"""Tail-managed short-variance daily P&L per ccy. Gate uses DVOL up to t-1 (no lookahead)."""
days = sorted(set(iv) & set(close)); pnl = {}
w0 = max(cfg["vrp_level_win"], cfg["vrp_rise_k"]) + 1
for i in range(w0, len(days)):
d0, d1 = days[i - 1], days[i]
if close[d0] <= 0 or close[d1] <= 0:
continue
r = math.log(close[d1] / close[d0]); ivl = iv[d0] / 100.0
raw = ivl * ivl / 365.0 - r * r # collect implied var, pay realized
rising = iv[d0] > iv[days[i - 1 - cfg["vrp_rise_k"]]] # don't sell into rising vol
win = [iv[days[j]] for j in range(i - 1 - cfg["vrp_level_win"], i - 1)]
level = 1.0 if iv[d0] < float(np.median(win)) else 0.5 # full in calm, half elevated
pnl[d1] = (0.0 if rising else 1.0) * level * raw
return pnl
def vrp_book_series(cfg, btc_close, eth_close, refresh):
b = vrp_ccy(_dvol("BTC", refresh), btc_close, cfg)
e = vrp_ccy(_dvol("ETH", refresh), eth_close, cfg)
days = sorted(set(b) | set(e))
return {d: float(np.nanmean([b.get(d, np.nan), e.get(d, np.nan)])) for d in days}
def _closes_from(syms, days, close, sym):
if sym not in syms:
return {}
j = syms.index(sym)
return {int(days[t]): float(close[t, j]) for t in range(len(days)) if np.isfinite(close[t, j])}
def _vrp_calib(cfg):
"""From cached history: (momentum daily vol, VRP daily vol) — to size VRP to its risk budget."""
syms, days, close, qv, fund = pit_sweep.load()
R = np.zeros_like(close); R[1:] = np.log(close)[1:] - np.log(close)[:-1]; R = np.where(np.isfinite(R), R, 0.0)
cf = np.where(np.isfinite(fund), fund, 0.0)
w, _ = compute_weights(close, qv, days, cfg)
mom = np.sum(w[:-1] * (R - cf)[1:], axis=1)
vrpb = vrp_book_series(cfg, _closes_from(syms, days, close, "BTCUSDT"),
_closes_from(syms, days, close, "ETHUSDT"), refresh=False)
return float(np.nanstd(mom)), float(np.nanstd(np.array(list(vrpb.values()))))
# ---------------------------------------------------------------- BACKTEST
def backtest(cfg):
syms, days, close, qv, fund = pit_sweep.load()
T, N = close.shape
R = np.zeros((T, N)); R[1:] = np.log(close)[1:] - np.log(close)[:-1]
R = np.where(np.isfinite(R), R, 0.0)
fund = np.where(np.isfinite(fund), fund, 0.0)
tradeable = np.ones((T, N), bool)
for j in range(N):
idx = np.where(np.isfinite(close[:, j]))[0]
if len(idx):
tradeable[max(0, idx[-1] - 4):idx[-1] + 1, j] = False
Reff = np.where(tradeable, R - fund, 0.0)
dv = roll(np.mean, np.nan_to_num(qv), 30)
vol = roll(np.std, R, cfg["vol_win"])
w, univ = compute_weights(close, qv, days, cfg)
gross = np.sum(w[:-1] * Reff[1:], axis=1)
net = slippage_net(w, Reff, dv, vol, days, cfg)
year = (1970 + days / 365.25).astype(int)
v = validate(net, days, 30)
gsr = sharpe_t(torch.tensor(gross, device=DEV, dtype=torch.float64))
turn = float(np.mean(np.sum(np.abs(w[1:] - w[:-1]), axis=1))) * 100
print(f"\n===== SURFER PoC BACKTEST — XS momentum (top{cfg['topk']}, {cfg['lookback']}d, weekly, smooth{cfg['smooth_span']}) =====")
print(f"coins(ever)={N} days={T} universe/day~{int(univ.sum(1).mean())} cost-AUM=${cfg['cost_aum']/1e6:.0f}M turnover/day={turn:.1f}%")
print(f"gross Sharpe {gsr:+.2f} | NET Sharpe {v['full']:+.2f} IS {v['is_']:+.2f} OOS {v['oos']:+.2f} CPCVmed {v['med']:+.2f} DSR {v['dsr']:.2f}")
print(f"max-DD (sum-rets) {max_drawdown(net):+.2f}")
py = [sharpe_t(torch.tensor(net[year[1:] == y], device=DEV, dtype=torch.float64)) if (year[1:] == y).sum() > 30 else float('nan') for y in range(2020, 2027)]
print("per-year 2020..26: " + " ".join(f"{p:+.2f}" if not math.isnan(p) else " n/a" for p in py))
print("capacity: " + " ".join(
f"${a/1e6:.0f}M={sharpe_t(torch.tensor(slippage_net(w,Reff,dv,vol,days,{**cfg,'cost_aum':a}),device=DEV,dtype=torch.float64)):+.2f}"
for a in [1e6, 5e6, 2e7, 5e7, 2e8]))
# ---- VRP diversifier sleeve + combined two-sleeve book ----
vrpb = vrp_book_series(cfg, _closes_from(syms, days, close, "BTCUSDT"),
_closes_from(syms, days, close, "ETHUSDT"), refresh=False)
mom_by = {int(days[1:][i]): net[i] for i in range(len(net))}
common = sorted(set(vrpb) & set(mom_by))
if common:
mo = np.array([mom_by[d] for d in common]); vr = np.array([vrpb[d] for d in common])
mu, vu, f = np.nanstd(mo), np.nanstd(vr), cfg["vrp_risk_frac"]
comb = (mo / mu) * (1 - f) + (vr / vu) * f
cy = (1970 + np.array(common) / 365.25).astype(int)
T = lambda x: torch.tensor(x, device=DEV, dtype=torch.float64)
print(f"VRP sleeve (tail-managed) Sharpe {sharpe_t(T(vr)):+.2f} corr-to-momentum {np.corrcoef(mo,vr)[0,1]:+.2f}")
print(f"COMBINED BOOK (momentum {int((1-f)*100)}% + VRP {int(f*100)}% risk) Sharpe {sharpe_t(T(comb)):+.2f}")
print("combined per-year: " + " ".join(
f"{y}:{sharpe_t(T(comb[cy==y])):+.2f}" for y in range(2021, 2027) if (cy == y).sum() > 30))
# ---------------------------------------------------------------- REGIME DIAGNOSTIC (overlay default OFF)
def regime(cfg):
syms, days, close, qv, fund = pit_sweep.load()
T, N = close.shape
R = np.zeros((T, N)); R[1:] = np.log(close)[1:] - np.log(close)[:-1]; R = np.where(np.isfinite(R), R, 0.0)
fund = np.where(np.isfinite(fund), fund, 0.0)
w, univ = compute_weights(close, qv, days, cfg)
book = np.sum(w[:-1] * (np.where(np.isfinite(R), R, 0.0) - fund)[1:], axis=1) # daily book return
lc = np.log(close)
disp = np.array([np.nanstd(trailing(lc, cfg["lookback"])[t][univ[t]]) if univ[t].any() else np.nan for t in range(T)])
mvol = np.nanmedian(roll(np.std, R, cfg["vol_win"]), axis=1)
mtrend = np.array([np.nanmedian(np.abs(trailing(lc, cfg["lookback"])[t][univ[t]])) if univ[t].any() else np.nan for t in range(T)])
feats = {"xs_dispersion": disp[:-1], "median_vol": mvol[:-1], "abs_trend": mtrend[:-1]}
split = int(0.7 * len(book))
print("\n===== REGIME DIAGNOSTIC — does a feature predict next-day book return? (overlay OFF until confirmed) =====")
print(f"{'feature':>16} {'IC_all':>7} {'IC_IS':>7} {'IC_OOS':>7}")
for nm, f in feats.items():
f = f[1:]; b = book[1:] # feature_t -> book return_{t+1}
m = np.isfinite(f) & np.isfinite(b)
def ic(mask):
mm = m & mask
return float(np.corrcoef(f[mm], b[mm])[0, 1]) if mm.sum() > 50 else float("nan")
idx = np.arange(len(f))
print(f"{nm:>16} {ic(np.ones_like(m)):>+7.3f} {ic(idx < split):>+7.3f} {ic(idx >= split):>+7.3f}")
print("Significant + IS/OOS-consistent IC => regime sizing worth building. Otherwise momentum runs flat-sized.")
# ---------------------------------------------------------------- LIVE (paper)
def _get(u):
return json.load(urllib.request.urlopen(urllib.request.Request(u, headers={"User-Agent": "curl/8"}), timeout=30))
def _live_panel(cfg):
info = _get("https://fapi.binance.com/fapi/v1/exchangeInfo")
perps = {s["symbol"] for s in info["symbols"] if s.get("contractType") == "PERPETUAL"
and s.get("quoteAsset") == "USDT" and s.get("status") == "TRADING"}
tick = _get("https://fapi.binance.com/fapi/v1/ticker/24hr")
vol = {t["symbol"]: float(t["quoteVolume"]) for t in tick if t["symbol"] in perps and t["symbol"].isascii()}
syms = sorted(vol, key=lambda s: -vol[s])[:max(cfg["topk"] * 2, 60)] # wide net; signal picks top-K
need = max(cfg["lookback"] + cfg["vol_win"], cfg["vrp_level_win"] + cfg["vrp_rise_k"],
cfg["beta_win"] + cfg["lookback"]) + 12 # cover VRP gate + residual-momentum beta window
closes, qvols, funds, keep = {}, {}, {}, []
for s in syms:
k = _get(f"https://fapi.binance.com/fapi/v1/klines?symbol={s}&interval=1d&limit={need}")
if len(k) < need - 2:
continue
d = np.array([int(r[0]) // DAY_MS for r in k])
closes[s] = (d, np.array([float(r[4]) for r in k]), np.array([float(r[7]) for r in k]))
fr = _get(f"https://fapi.binance.com/fapi/v1/fundingRate?symbol={s}&limit=10")
funds[s] = float(fr[-1]["fundingRate"]) * 3 if fr else 0.0 # ~daily funding (3x 8h)
keep.append(s)
time.sleep(0.05)
days = np.array(sorted(set().union(*[set(closes[s][0].tolist()) for s in keep])))
di = {int(v): i for i, v in enumerate(days.tolist())}
T, N = len(days), len(keep)
close = np.full((T, N), np.nan); qv = np.full((T, N), np.nan)
for j, s in enumerate(keep):
d, c, q = closes[s]
for i in range(len(d)):
close[di[int(d[i])], j] = c[i]; qv[di[int(d[i])], j] = q[i]
return keep, days, close, qv, np.array([funds[s] for s in keep])
def _load_state():
if os.path.exists(STATE):
return json.load(open(STATE))
return dict(inception=None, last_mark_day=None, positions={}, prices={}, equity=1.0, log=[])
def _save_state(st):
tmp = STATE + ".tmp"
json.dump(st, open(tmp, "w"), indent=1)
os.replace(tmp, STATE)
def paper_step(cfg, state):
"""Value-returning core of `paper()` for the fxhnt ForwardTracker — IDENTICAL mark/rebalance/VRP math,
but takes the prior working `state` dict in and returns `(comb_ret, today_epoch, new_state, info)` instead
of loading/saving a file and printing. `comb_ret` is the booked combined return for `today` (0.0 when no
mark happened — first run / already marked today). `info` carries (rebal, trades, vrp_today, target) for an
optional caller-side print. The returned mutated `state` is exactly what `paper()` would have persisted."""
keep, days, close, qv, fund_now = _live_panel(cfg)
w, univ = compute_weights(close, qv, days, cfg)
target = {keep[j]: float(w[-1, j]) for j in range(len(keep)) if abs(w[-1, j]) > 1e-6}
last_close = {keep[j]: float(close[-1, j]) for j in range(len(keep)) if np.isfinite(close[-1, j])}
today = int(days[-1])
st = state
if st["inception"] is None:
st["inception"] = today
st.setdefault("vrp_equity", 1.0); st.setdefault("combined_equity", 1.0)
if "vrp_vu" not in st:
try:
st["vrp_mu"], st["vrp_vu"] = _vrp_calib(cfg)
except Exception:
st["vrp_mu"], st["vrp_vu"] = 0.0, 1.0
vrpb = vrp_book_series(cfg, _closes_from(keep, days, close, "BTCUSDT"),
_closes_from(keep, days, close, "ETHUSDT"), refresh=True)
vrp_today = vrpb.get(today, None)
comb_ret = 0.0
# 1) MARK both sleeves to latest prices (+ funding) since last mark
if st["positions"] and st["last_mark_day"] != today:
ret = 0.0
for s, pos in st["positions"].items():
p0 = st["prices"].get(s); p1 = last_close.get(s)
if p0 and p1 and p0 > 0:
ret += pos * (p1 / p0 - 1.0)
ret -= pos * fund_now[keep.index(s)] if s in keep else 0.0 # long pays funding
f = cfg["vrp_risk_frac"]
scaled = (vrp_today / st["vrp_vu"]) * st["vrp_mu"] if (vrp_today is not None and st["vrp_vu"] > 0) else 0.0
comb_ret = (1 - f) * ret + f * scaled # VRP scaled to momentum vol, f risk
st["equity"] *= (1.0 + ret)
st["vrp_equity"] *= (1.0 + scaled)
st["combined_equity"] *= (1.0 + comb_ret)
st["log"].append({"day": today, "mom_ret": round(ret, 6), "vrp_ret": round(scaled, 6),
"comb_ret": round(comb_ret, 6), "combined": round(st["combined_equity"], 5)})
# 2) REBALANCE on the weekly boundary
rebal = (st["last_mark_day"] is None) or (today % cfg["rebal_k"] == 0) # fixed weekly phase (Thursdays)
trades = []
if rebal:
cur = st["positions"]
allk = set(cur) | set(target)
for s in sorted(allk):
d = target.get(s, 0.0) - cur.get(s, 0.0)
if abs(d) > 1e-4:
trades.append({"sym": s, "side": "BUY" if d > 0 else "SELL", "dweight": round(d, 4)})
st["positions"] = target
st["prices"] = last_close
st["last_mark_day"] = today
return comb_ret, today, st, dict(rebal=rebal, trades=trades, vrp_today=vrp_today, target=target, keep=keep)
def paper(cfg):
st = _load_state()
comb_ret, today, st, info = paper_step(cfg, st)
rebal, trades, vrp_today, target, keep = (info["rebal"], info["trades"], info["vrp_today"],
info["target"], info["keep"])
_save_state(st)
print(f"\n===== SURFER PoC PAPER — day {today} (universe {len(keep)} perps) =====")
print(f"momentum {st['equity']:.4f} | VRP {st['vrp_equity']:.4f} | COMBINED BOOK {st['combined_equity']:.4f} "
f"(inception day {st['inception']}, {len(st['log'])} marks)")
print(f"VRP today: {'flat (gated)' if (vrp_today is not None and abs(vrp_today)<1e-9) else ('%.5f'%vrp_today if vrp_today is not None else 'n/a')} "
f"rebalance: {rebal} intended trades: {len(trades)}")
longs = sorted([(s, x) for s, x in target.items() if x > 0], key=lambda z: -z[1])[:8]
shorts = sorted([(s, x) for s, x in target.items() if x < 0], key=lambda z: z[1])[:8]
print("top longs : " + ", ".join(f"{s} {x:+.3f}" for s, x in longs))
print("top shorts: " + ", ".join(f"{s} {x:+.3f}" for s, x in shorts))
for t in trades[:12]:
print(f" {t['side']:>4} {t['sym']:>12} dW {t['dweight']:+.4f}")
print("(paper only — no real orders. Re-run weekly to forward-test edge persistence.)")
def status(cfg):
st = _load_state()
print(f"\n===== SURFER PoC STATUS =====")
if st["inception"] is None:
print("no paper state yet — run: python scripts/surfer/surfer_poc.py paper"); return
print(f"inception day {st['inception']} last mark {st['last_mark_day']} marks {len(st['log'])}")
print(f"equity: momentum {st['equity']:.4f} | VRP {st.get('vrp_equity',1.0):.4f} | COMBINED {st.get('combined_equity',1.0):.4f}")
if st["log"]:
def srp(key):
r = np.array([m[key] for m in st["log"] if key in m])
return r.mean() / (r.std() + 1e-9) * math.sqrt(252) if len(r) > 5 else float("nan")
print(f"annual-ish Sharpe (live): momentum {srp('mom_ret'):+.2f} | VRP {srp('vrp_ret'):+.2f} | COMBINED {srp('comb_ret'):+.2f}")
print(f"current book: {len([p for p in st['positions'].values() if abs(p)>1e-6])} positions")
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument("mode", choices=["backtest", "regime", "paper", "status"])
a = ap.parse_args()
{"backtest": backtest, "regime": regime, "paper": paper, "status": status}[a.mode](CFG)

59
tests/integration/test_forward_tracker.py Normal file
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@@ -0,0 +1,59 @@
"""Generic ForwardTracker: first-run freeze, forward-only booking, idempotency, extra round-trip."""
from __future__ import annotations
from fxhnt.application.forward_tracker import ForwardTracker
class _Recomputable:
"""Recomputable-series strategy: always returns the full known series."""
def __init__(self, series: list[tuple[str, float]]) -> None:
self._series = series
def advance(self, last_date, extra):
return list(self._series), extra
class _LiveBooking:
"""Live-booking strategy: books one row for `today`, carries a counter in extra."""
def __init__(self, today: str, ret: float) -> None:
self._today, self._ret = today, ret
def advance(self, last_date, extra):
n = int(extra.get("runs", 0)) + 1
return [(self._today, self._ret)], {"runs": n}
def test_first_run_freezes_and_books_nothing(tmp_path) -> None:
p = str(tmp_path / "s.json")
st = ForwardTracker(_Recomputable([("2026-01-01", 0.01), ("2026-01-02", 0.02)]), p).step()
assert st.forward_days == 0 # freeze: nothing booked on first run
assert st.inception == "2026-01-02" # inception = latest known date
assert st.last_date == "2026-01-02"
def test_second_run_books_only_forward_days(tmp_path) -> None:
p = str(tmp_path / "s.json")
strat = _Recomputable([("2026-01-01", 0.01), ("2026-01-02", 0.02)])
ForwardTracker(strat, p).step() # freeze at 2026-01-02
strat._series.append(("2026-01-03", 0.05)) # a new day appears
st = ForwardTracker(strat, p).step()
assert st.forward_days == 1 # only 2026-01-03 booked
assert abs(st.forward_return_pct - 5.0) < 1e-9
assert st.last_date == "2026-01-03"
def test_idempotent_rerun_books_nothing(tmp_path) -> None:
p = str(tmp_path / "s.json")
strat = _Recomputable([("2026-01-01", 0.01), ("2026-01-02", 0.02), ("2026-01-03", 0.05)])
ForwardTracker(strat, p).step() # freeze at 2026-01-03
st = ForwardTracker(strat, p).step() # nothing new
assert st.forward_days == 0
def test_extra_carry_state_round_trips(tmp_path) -> None:
p = str(tmp_path / "s.json")
ForwardTracker(_LiveBooking("2026-01-01", 0.0), p).step() # freeze, extra={"runs":1}
ForwardTracker(_LiveBooking("2026-01-02", 0.01), p).step() # books 2026-01-02
import json
extra = json.load(open(p))["extra"]
assert extra["runs"] == 2 # advance saw prior extra and incremented

126
tests/integration/test_massive_daily.py Normal file
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"""MassiveDailyClient — total-return reconstruction from Massive (Polygon) split-only adjusted closes
+ dividends. NO live network: the `_get` HTTP layer is monkeypatched to return fixed fixtures.
The critical invariant proven here: on an ex-dividend day, the day-over-day return of the returned
total-return series equals the raw PRICE return PLUS dividend/prev_close — i.e. the dividend is added
back, matching Yahoo adjclose semantics. With no dividends, TR closes == split-adjusted closes.
"""
from __future__ import annotations
import datetime as dt
from typing import Any
from fxhnt.adapters.data.massive_daily import MassiveDailyClient
def _ms(date: str) -> int:
"""Midnight-UTC epoch milliseconds for a YYYY-MM-DD date (matches Massive aggregate `t`)."""
d = dt.datetime.strptime(date, "%Y-%m-%d").replace(tzinfo=dt.timezone.utc)
return int(d.timestamp() * 1000)
def _aggs(closes: dict[str, float]) -> dict[str, Any]:
return {"results": [{"t": _ms(d), "c": c} for d, c in sorted(closes.items())]}
def _divs(divs: list[tuple[str, float]]) -> dict[str, Any]:
return {"results": [{"ex_dividend_date": d, "cash_amount": c} for d, c in divs]}
def _install(client: MassiveDailyClient, closes: dict[str, float], divs: list[tuple[str, float]]) -> None:
"""Route `_get` by endpoint: aggregates vs dividends. No network."""
def fake_get(url: str, tries: int = 4) -> dict[str, Any]:
if "/v3/reference/dividends" in url:
return _divs(divs)
if "/v2/aggs/ticker/" in url:
return _aggs(closes)
raise AssertionError(f"unexpected url: {url}")
client._get = fake_get # type: ignore[method-assign]
def test_returns_date_to_price_dict() -> None:
client = MassiveDailyClient(api_key="x")
closes = {"2026-01-02": 100.0, "2026-01-03": 101.0, "2026-01-06": 102.0}
_install(client, closes, divs=[])
out = client.adj_closes("SPY")
assert isinstance(out, dict)
assert set(out) == set(closes)
assert all(isinstance(k, str) and isinstance(v, float) for k, v in out.items())
def test_no_dividends_returns_split_adjusted_unchanged() -> None:
client = MassiveDailyClient(api_key="x")
closes = {"2026-01-02": 100.0, "2026-01-03": 101.0, "2026-01-06": 102.0}
_install(client, closes, divs=[])
out = client.adj_closes("SPY")
for d, c in closes.items():
assert abs(out[d] - c) < 1e-12
def test_ex_div_day_return_includes_dividend() -> None:
"""The load-bearing test: a single $1.80 dividend ex-date 2026-01-03.
Raw price return on the ex-div day = c[03]/c[02] - 1.
Total-return on the ex-div day = tr[03]/tr[02] - 1, with the dividend added back: exactly
c_D/(prev_close - C) - 1 (canonical), ≈ price_return + dividend/prev_close (first order).
"""
cash = 1.80
closes = {"2026-01-02": 100.0, "2026-01-03": 99.0, "2026-01-06": 100.0}
client = MassiveDailyClient(api_key="x")
_install(client, closes, divs=[("2026-01-03", cash)])
out = client.adj_closes("SPY")
prev_close = closes["2026-01-02"]
price_return = closes["2026-01-03"] / prev_close - 1.0
tr_return = out["2026-01-03"] / out["2026-01-02"] - 1.0
# EXACT canonical (CRSP / Yahoo) back-adjustment identity: prior close scaled by
# (1 - C/prev_close) ⇒ ex-div-day total return = c_D/(prev_close - C) - 1, the dividend added back.
exact_tr_return = closes["2026-01-03"] / (prev_close - cash) - 1.0
assert abs(tr_return - exact_tr_return) < 1e-9
# First-order (additive) form the spec describes: price return PLUS dividend yield.
additive_tr_return = price_return + cash / prev_close
assert abs(tr_return - additive_tr_return) < 1e-3
# The most recent close is the anchor: factors only scale dates strictly before the ex-div date.
assert abs(out["2026-01-06"] - closes["2026-01-06"]) < 1e-12
assert abs(out["2026-01-03"] - closes["2026-01-03"]) < 1e-12
# Total return on an ex-div day is strictly higher than the raw price return.
assert tr_return > price_return
def test_multi_dividend_factors_compound() -> None:
"""Two dividends on different ex-dates: back-adjustment factors must COMPOUND.
Dates strictly before BOTH ex-divs receive both factors multiplied together; a date that lies
on/after the earlier ex-div but strictly before the later one receives ONLY the later factor; the
most-recent close (on the later ex-div date) is the anchor and is unscaled. This locks the
`factor[d] *= f` accumulation in MassiveDailyClient._total_return.
"""
# Split-adjusted closes on five consecutive trading dates.
closes = {
"2026-01-02": 100.0,
"2026-01-05": 101.0,
"2026-01-06": 99.0,
"2026-01-07": 102.0,
"2026-01-08": 100.0,
}
cash1 = 1.50 # ex-date 2026-01-06; trading day before = 2026-01-05 (close 101.0)
cash2 = 2.00 # ex-date 2026-01-08; trading day before = 2026-01-07 (close 102.0)
client = MassiveDailyClient(api_key="x")
_install(client, closes, divs=[("2026-01-06", cash1), ("2026-01-08", cash2)])
out = client.adj_closes("SPY")
f1 = 1.0 - cash1 / closes["2026-01-05"] # = 0.9851485148514851
f2 = 1.0 - cash2 / closes["2026-01-07"] # = 0.9803921568627451
# Strictly before BOTH ex-divs ⇒ BOTH factors compounded.
assert abs(out["2026-01-02"] - closes["2026-01-02"] * f1 * f2) < 1e-9 # 96.58318773053776
assert abs(out["2026-01-05"] - closes["2026-01-05"] * f1 * f2) < 1e-9 # 97.54901960784314
# On/after the earlier ex-div (2026-01-06) but strictly before the later (2026-01-08) ⇒ ONLY f2.
assert abs(out["2026-01-06"] - closes["2026-01-06"] * f2) < 1e-9 # 97.05882352941175
assert abs(out["2026-01-07"] - closes["2026-01-07"] * f2) < 1e-9 # 100.0
# The later ex-div date is the most-recent close: anchor, unscaled.
assert abs(out["2026-01-08"] - closes["2026-01-08"]) < 1e-9 # 100.0
# Sanity: the doubly-adjusted date is scaled strictly more than the singly-adjusted one (f1<1).
assert out["2026-01-05"] / closes["2026-01-05"] < out["2026-01-06"] / closes["2026-01-06"]

11
tests/integration/test_orchestration_definitions.py
View File

@@ -11,6 +11,9 @@ def test_definitions_load_with_assets_and_schedule() -> None:
repo = defs.get_repository_def()
asset_keys = {k.to_user_string() for k in repo.assets_defs_by_key}
assert {"crypto_bars", "futures_bars", "combined_forward_nav", "cockpit_forward"} <= asset_keys
# B1: the six paper-track assets are wired into the graph alongside the B0 four
paper = {"sixtyforty_nav", "multistrat_nav", "gd_nav", "funding_nav", "crossvenue_nav", "poc_nav"}
assert paper <= asset_keys, f"missing paper-track assets: {paper - asset_keys}"
# --- schedule present with the right cron ---
# defs.schedules is a list[ScheduleDefinition] (or None when empty)
@@ -22,3 +25,11 @@ def test_definitions_load_with_assets_and_schedule() -> None:
from dagster import DefaultScheduleStatus
daily = next(s for s in scheds if getattr(s, "cron_schedule", "") == "30 23 * * *")
assert daily.default_status == DefaultScheduleStatus.RUNNING
# --- cockpit_forward depends on every paper-track asset (so it ingests their state files) ---
from dagster import AssetKey
cockpit_def = repo.assets_defs_by_key[AssetKey("cockpit_forward")]
deps = cockpit_def.asset_deps[AssetKey("cockpit_forward")]
upstream = {k.to_user_string() for k in deps}
expected_upstream = {"combined_forward_nav"} | paper
assert expected_upstream <= upstream, f"cockpit_forward missing upstream: {expected_upstream - upstream}"

513
tests/integration/test_paper_strategies.py Normal file
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"""Paper-forward strategy services: deterministic domain + service over fake data ports, and a
round-trip through ForwardStateReader so the cockpit contract is verified."""
from __future__ import annotations
import json
from pathlib import Path
from fxhnt.adapters.persistence.state_reader import ForwardStateReader
from fxhnt.application.forward_tracker import ForwardTracker
from fxhnt.application.paper_strategies import (
CrossVenueStrategy,
FundingCarryStrategy,
GrowthDisciplineStrategy,
MomentumVrpStrategy,
SixtyFortyStrategy,
)
class FakeDailyBars:
def __init__(self, data: dict[str, dict[str, float]]) -> None:
self._data = data
def adj_closes(self, symbol: str) -> dict[str, float]:
return self._data[symbol]
class FakeBinanceFunding:
"""Deterministic BinanceFundingClient: fixed (liq, tf30, last24) scan output.
liq is the liquid/native/hedgeable universe ({sym: 24h_quote_volume}); tf30 is the trailing-30d-mean
daily funding (the qualify signal); last24 is today's booking rate (the book_return signal)."""
def __init__(
self,
liq: dict[str, float],
tf30: dict[str, float],
last24: dict[str, float],
) -> None:
self._liq = liq
self._tf30 = tf30
self._last24 = last24
def scan(self, prev: set[str]) -> tuple[dict[str, float], dict[str, float], dict[str, float]]:
return dict(self._liq), dict(self._tf30), dict(self._last24)
class FakeCrossVenue:
"""Deterministic CrossVenueFundingClient: fixed per-venue funding + volume, no network.
snapshot() returns (funding, volume) from one call — funding: {coin: {venue: daily_funding_rate}}
(already normalized to DAILY, as the live adapter returns); volume: {coin: {venue: 24h_quote_volume_usd}}."""
def __init__(
self,
funding: dict[str, dict[str, float]],
volume: dict[str, dict[str, float]],
) -> None:
self._funding = funding
self._volume = volume
def snapshot(self) -> tuple[dict[str, dict[str, float]], dict[str, dict[str, float]]]:
return (
{c: dict(v) for c, v in self._funding.items()},
{c: dict(v) for c, v in self._volume.items()},
)
def test_sixtyforty_daily_return_is_weighted_mean() -> None:
from fxhnt.domain.strategies.sixtyforty import daily_returns
closes = {
"SPY": {"2026-01-01": 100.0, "2026-01-02": 110.0}, # +10%
"IEF": {"2026-01-01": 100.0, "2026-01-02": 105.0}, # +5%
}
rows = daily_returns(closes, [("SPY", 0.6), ("IEF", 0.4)])
assert len(rows) == 1 and rows[0][0] == "2026-01-02"
assert abs(rows[0][1] - (0.6 * 0.10 + 0.4 * 0.05)) < 1e-12 # 0.08
def test_sixtyforty_service_round_trips_through_reader(tmp_path) -> None:
bars = FakeDailyBars({
"SPY": {"2026-01-01": 100.0, "2026-01-02": 110.0, "2026-01-03": 110.0},
"IEF": {"2026-01-01": 100.0, "2026-01-02": 105.0, "2026-01-03": 105.0},
})
p = str(tmp_path / "sixtyforty_state.json")
ForwardTracker(SixtyFortyStrategy(bars), p).step() # freeze at 2026-01-03
bars._data["SPY"]["2026-01-04"] = 121.0 # +10%
bars._data["IEF"]["2026-01-04"] = 105.0 # 0%
st = ForwardTracker(SixtyFortyStrategy(bars), p).step()
assert st.forward_days == 1
summary, rows = ForwardStateReader().read(p, "sixtyforty")
assert summary.days == 1
assert abs(summary.nav - (1.0 + 0.06)) < 1e-9 # 0.6*0.10 + 0.4*0
def test_multistrat_vol_targets_and_caps_leverage() -> None:
import datetime
import math
import numpy as np
from fxhnt.domain.strategies.multistrat import INSTRUMENTS, book_series
rng = np.random.default_rng(0)
# Faithful port keeps the original 63-day warmup (volnorm/trust windows); need >> 64 dates
# for any day to be booked, so generate ~10 months of weekday-ish dates.
start = datetime.date(2025, 1, 1)
dates = [(start + datetime.timedelta(days=i)).isoformat() for i in range(300)]
closes = {}
for k, s in enumerate(INSTRUMENTS):
steps = rng.normal(0.0003 * (1 if k % 2 else -1), 0.01, len(dates))
px = 100.0 * np.cumprod(1 + steps)
closes[s] = {d: float(px[i]) for i, d in enumerate(dates)}
rows = book_series(closes)
rets = np.array([r for _, r in rows])
assert len(rows) >= 1 and np.isfinite(rets).all()
vol = float(rets.std() * math.sqrt(252))
# Faithful port is UNLEVERED (MAXLEV=1.0): on this low-cross-corr synthetic data the trust-weighted
# combo diversifies down to ~0.047 vol, so the unlevered book legitimately realizes ~0.038 — below
# the 0.10 target because leverage is capped at 1.0 and cannot lever the combo back up. The invariant
# tested is that the book is vol-CONTROLLED (well under the cap, never blowing past target), not that
# it hits 0.10 exactly. Verified bit-for-bit identical to the foxhunt original on this data.
assert 0.02 <= vol <= 0.15 # vol-controlled (unlevered book; widened per faithful port)
def test_growth_discipline_blend_matches_w_qqq_plus_multistrat() -> None:
import datetime
import numpy as np
from fxhnt.domain.strategies.growth_discipline import daily_returns
from fxhnt.domain.strategies.multistrat import INSTRUMENTS, book_series
rng = np.random.default_rng(1)
# Enough dates that multistrat's 63-day warmup still leaves booked days (mirror multistrat test).
start = datetime.date(2025, 1, 1)
dates = [(start + datetime.timedelta(days=i)).isoformat() for i in range(300)]
closes = {}
for k, s in enumerate(INSTRUMENTS):
steps = rng.normal(0.0003 * (1 if k % 2 else -1), 0.01, len(dates))
px = 100.0 * np.cumprod(1 + steps)
closes[s] = {d: float(px[i]) for i, d in enumerate(dates)}
# QQQ shares the same date grid so its consecutive-day return aligns with the book's dates exactly.
qsteps = rng.normal(0.0004, 0.011, len(dates))
qpx = 100.0 * np.cumprod(1 + qsteps)
qqq = {d: float(qpx[i]) for i, d in enumerate(dates)}
w = 0.70
rows = daily_returns(qqq, closes, w)
gd = dict(rows)
# Reconstruct the expectation: r_multistrat from book_series, r_QQQ as consecutive-day return.
ms = dict(book_series(closes))
qdates = sorted(qqq)
qret = {qdates[i]: qqq[qdates[i]] / qqq[qdates[i - 1]] - 1.0 for i in range(1, len(qdates))}
expected = {d: w * qret[d] + (1.0 - w) * ms[d] for d in (set(ms) & set(qret))}
common = set(gd) & set(expected)
assert len(common) >= 1
for d in common:
assert abs(gd[d] - expected[d]) < 1e-9
def test_growth_discipline_service_round_trips_through_reader(tmp_path) -> None:
import datetime
import numpy as np
from fxhnt.domain.strategies.multistrat import INSTRUMENTS
rng = np.random.default_rng(2)
start = datetime.date(2025, 1, 1)
dates = [(start + datetime.timedelta(days=i)).isoformat() for i in range(300)]
data = {}
for k, s in enumerate(INSTRUMENTS):
steps = rng.normal(0.0003 * (1 if k % 2 else -1), 0.01, len(dates))
px = 100.0 * np.cumprod(1 + steps)
data[s] = {d: float(px[i]) for i, d in enumerate(dates)}
qsteps = rng.normal(0.0004, 0.011, len(dates))
qpx = 100.0 * np.cumprod(1 + qsteps)
data["QQQ"] = {d: float(qpx[i]) for i, d in enumerate(dates)}
bars = FakeDailyBars(data)
p = str(tmp_path / "gd_state.json")
ForwardTracker(GrowthDisciplineStrategy(bars), p).step() # freeze at inception
# Append one new trading day across every series so the second step books >= 1 day.
nd = (start + datetime.timedelta(days=len(dates))).isoformat()
for s in list(INSTRUMENTS) + ["QQQ"]:
last = data[s][dates[-1]]
data[s][nd] = last * 1.001
ForwardTracker(GrowthDisciplineStrategy(bars), p).step()
summary, rows = ForwardStateReader().read(p, "gd")
assert summary.days == len(rows)
assert summary.days >= 1
def test_funding_qualify_on_tf30_and_book_return_on_last24() -> None:
from fxhnt.domain.strategies.funding_carry import book_return, qualify
# `liq` is the already-filtered liquid/native/hedgeable universe (keys = qualify candidates,
# values = 24h quote volume, unused by qualify).
liq = {"AAAUSDT": 10e6, "BBBUSDT": 10e6, "DDDUSDT": 10e6}
# qualify reads tf30 (trailing-30d MEAN), NOT today's last24.
# AAA: tf30 above hurdle (new opens)
# BBB: tf30 between exit(3bp) and hurdle(5bp) (only stays if held)
# DDD: tf30 below exit, BUT a huge last24 today — must NOT qualify (qualify ignores last24).
tf30 = {"AAAUSDT": 0.0006, "BBBUSDT": 0.0004, "DDDUSDT": 0.0002}
last24 = {"AAAUSDT": 0.0006, "BBBUSDT": 0.0004, "DDDUSDT": 0.0090} # DDD spikes today but low tf30
# No prior holdings: only AAA qualifies (BBB below hurdle, DDD below exit despite high last24).
assert qualify(liq, tf30, prev={}) == {"AAAUSDT": 0.0006}
# BBB held and still above exit_hurdle on tf30 → kept; AAA still opens; DDD dropped.
assert sorted(qualify(liq, tf30, prev={"BBBUSDT": 0.5})) == ["AAAUSDT", "BBBUSDT"]
# A held coin whose tf30 is high last24 but low tf30 still does NOT qualify (proves tf30 is the gate).
assert qualify(liq, tf30, prev={"DDDUSDT": 0.5}) == {"AAAUSDT": 0.0006}
# book_return uses last24 (today's booking rate), NOT tf30: prior equal-weight book of 2 coins
# (w=0.5 each), rebalance to a single-coin book → realized minus cost on turnover (COST_RT/2).
prev = {"AAAUSDT": 0.5, "BBBUSDT": 0.5}
newpos = {"AAAUSDT": 1.0}
realized = 0.5 * 0.0006 + 0.5 * 0.0004 # last24 of AAA and BBB
turnover = abs(1.0 - 0.5) + abs(0.0 - 0.5) # AAA up 0.5, BBB out 0.5
expected = realized - turnover * (1e-3 / 2)
assert abs(book_return(prev, last24, newpos) - expected) < 1e-15
def test_funding_service_round_trips_through_reader(tmp_path) -> None:
liq = {"AAAUSDT": 10e6, "BBBUSDT": 10e6}
tf30 = {"AAAUSDT": 0.0006, "BBBUSDT": 0.0006} # both above hurdle on the 30d mean
last24 = {"AAAUSDT": 0.0006, "BBBUSDT": 0.0006} # today's booking rate
fake = FakeBinanceFunding(liq, tf30, last24)
p = str(tmp_path / "funding_state.json")
# Live-booking books rows dated by the wall clock. On the freeze run rows are empty so the tracker
# freezes inception at its own _today_iso; the second run must book a row dated strictly after that.
# Inject a clock returning a day after inception (real cron runs on later calendar days).
day = [""]
# First step freezes (books nothing) but seeds initial equal-weight positions in extra.
st0 = ForwardTracker(FundingCarryStrategy(fake, clock=lambda: day[0]), p).step()
assert st0.forward_days == 0
loaded0 = json.loads(Path(p).read_text())
assert loaded0["extra"]["positions"] == {"AAAUSDT": 0.5, "BBBUSDT": 0.5}
# Next day: bump today's booking rate so the booked carry differs; second step books exactly one row.
fake._last24["AAAUSDT"] = 0.0008
fake._last24["BBBUSDT"] = 0.0008
day[0] = "2999-01-01" # strictly after the frozen inception → booked
st1 = ForwardTracker(FundingCarryStrategy(fake, clock=lambda: day[0]), p).step()
assert st1.forward_days == 1
summary, rows = ForwardStateReader().read(p, "funding")
assert summary.days == 1
loaded1 = json.loads(Path(p).read_text())
# Still both qualify → equal-weight book of two positions.
assert loaded1["extra"]["positions"] == {"AAAUSDT": 0.5, "BBBUSDT": 0.5}
def test_crossvenue_spread_filters_liq_and_distress() -> None:
from fxhnt.domain.strategies.cross_venue import LIQ, MAXF, compute_spreads
funding = {
# AAA: liquid on Binance(+12bp) and Bybit(+2bp) → spread 10bp, short Binance, long Bybit.
"AAA": {"Binance": 0.0012, "Bybit": 0.0002},
# BBB: HL leg is below LIQ → only 1 venue passes → dropped (need >=2).
"BBB": {"Binance": 0.0020, "HL": 0.0001},
# CCC: Binance leg is distress (|f| > MAXF) → excluded → only 1 venue → dropped.
"CCC": {"Binance": 0.0060, "Bybit": 0.0003},
# DDD: three liquid non-distress venues → spread = max-min across all three.
"DDD": {"Binance": 0.0008, "Bybit": -0.0002, "HL": 0.0003},
}
volume = {
"AAA": {"Binance": 20e6, "Bybit": 20e6},
"BBB": {"Binance": 20e6, "HL": 1e6}, # HL below LIQ
"CCC": {"Binance": 20e6, "Bybit": 20e6}, # Binance distress on funding
"DDD": {"Binance": 20e6, "Bybit": 20e6, "HL": 20e6},
}
assert LIQ == 10e6 and MAXF == 0.005
rows = compute_spreads(funding, volume)
by_coin = {r["coin"]: r for r in rows}
assert set(by_coin) == {"AAA", "DDD"}
assert abs(by_coin["AAA"]["spread"] - (0.0012 - 0.0002)) < 1e-15
assert by_coin["AAA"]["short"] == "Binance" and by_coin["AAA"]["long"] == "Bybit"
assert abs(by_coin["DDD"]["spread"] - (0.0008 - (-0.0002))) < 1e-15
assert by_coin["DDD"]["short"] == "Binance" and by_coin["DDD"]["long"] == "Bybit"
def test_crossvenue_select_hysteresis_topk() -> None:
from fxhnt.domain.strategies.cross_venue import ENTRY, EXIT, TOPK, select
assert ENTRY == 0.0010 and EXIT == 0.0005 and TOPK == 10
# cur spreads: AAA strong-fresh-open; BBB between EXIT and ENTRY (only kept if held); CCC below EXIT.
cur = {"AAA": 0.0020, "BBB": 0.0007, "CCC": 0.0003, "DDD": 0.0015}
# No prior: only coins with spread > ENTRY open (AAA, DDD), sorted by spread desc, capped at TOPK.
assert sorted(select(cur, prev={})) == ["AAA", "DDD"]
# BBB held and still > EXIT → kept; AAA/DDD also open; CCC (held) below EXIT → dropped.
assert sorted(select(cur, prev={"BBB": 0.5, "CCC": 0.5})) == ["AAA", "BBB", "DDD"]
# TOPK cap: 12 fresh coins all above ENTRY, none held → only the top-10 by spread are taken.
many = {f"C{i:02d}": 0.0010 + i * 1e-5 for i in range(12)} # C11 highest ... C00 lowest
chosen = select(many, prev={})
assert len(chosen) == TOPK
assert set(chosen) == {f"C{i:02d}" for i in range(2, 12)} # top-10 spreads
def test_crossvenue_book_return_realized_minus_turnover_cost() -> None:
from fxhnt.domain.strategies.cross_venue import COST_RT, book_return
assert COST_RT == 0.0010
# Prior equal-weight book of 2 coins; today's spreads (cur) carry on the prior book.
prev = {"AAA": 0.5, "BBB": 0.5}
cur = {"AAA": 0.0020, "BBB": 0.0006, "DDD": 0.0030} # DDD not in prev → not realized
newpos = {"AAA": 1.0} # rebalance to single coin
realized = 0.5 * 0.0020 + 0.5 * 0.0006 # carry on prior book at today's spread
turnover = abs(1.0 - 0.5) + abs(0.0 - 0.5) # AAA up 0.5, BBB out 0.5
expected = realized - turnover * (COST_RT / 2)
assert abs(book_return(prev, cur, newpos) - expected) < 1e-15
def test_crossvenue_service_round_trips_through_reader(tmp_path) -> None:
# Two coins, each liquid + non-distress on two venues, spread > ENTRY → both open.
funding = {
"AAA": {"Binance": 0.0015, "Bybit": 0.0001}, # spread 14bp
"BBB": {"Binance": 0.0013, "Bybit": 0.0001}, # spread 12bp
}
volume = {
"AAA": {"Binance": 20e6, "Bybit": 20e6},
"BBB": {"Binance": 20e6, "Bybit": 20e6},
}
fake = FakeCrossVenue(funding, volume)
p = str(tmp_path / "crossvenue_state.json")
day = [""]
# First step freezes (books nothing) but seeds initial equal-weight positions.
st0 = ForwardTracker(CrossVenueStrategy(fake, clock=lambda: day[0]), p).step()
assert st0.forward_days == 0
loaded0 = json.loads(Path(p).read_text())
assert loaded0["extra"]["positions"] == {"AAA": 0.5, "BBB": 0.5}
day[0] = "2999-01-01" # strictly after frozen inception → booked
st1 = ForwardTracker(CrossVenueStrategy(fake, clock=lambda: day[0]), p).step()
assert st1.forward_days == 1
summary, rows = ForwardStateReader().read(p, "crossvenue")
assert summary.days == 1
loaded1 = json.loads(Path(p).read_text())
assert loaded1["extra"]["positions"] == {"AAA": 0.5, "BBB": 0.5}
# ---------------------------------------------------------------- poc (surfer momentum + VRP, faithful)
def test_xs_weights_market_neutral_unit_gross() -> None:
"""Real signal_sweep.xs_weights on a small array → cross-sectionally market-neutral, unit gross."""
import numpy as np
from fxhnt.vendor.surfer.signal_sweep import xs_weights
sig = np.array(
[
[1.0, 2.0, 3.0, 4.0, 5.0],
[-2.0, 0.0, 1.0, 3.0, -1.0],
[10.0, -5.0, 2.0, 7.0, -3.0],
]
)
w = xs_weights(sig)
assert w.shape == sig.shape
# market-neutral: each row sums ~0; unit gross: sum|w| ~1 per row.
for t in range(w.shape[0]):
assert abs(float(w[t].sum())) < 1e-9
assert abs(float(np.abs(w[t]).sum()) - 1.0) < 1e-9
def test_compute_weights_shape() -> None:
"""Real surfer_poc.compute_weights on a synthetic 50x80 panel.
Invariants of the faithful signal: shape preserved; the per-day *target* (pre-smoothing) xs_weights is
universe-restricted so its nonzeros ≤ topk; and the held (EWMA-smoothed, weekly-held) book stays
cross-sectionally market-neutral (row-sum ≈ 0) — EWMA is a linear combo of zero-sum target rows so it
cannot break neutrality even though it carries weights across day-to-day universe rotation (so the
SMOOTHED book can legitimately have > topk nonzeros)."""
import numpy as np
from fxhnt.vendor.surfer.surfer_poc import CFG, compute_weights
rng = np.random.default_rng(7)
T, N = 50, 80
close = 100.0 * np.cumprod(1.0 + rng.normal(0.0, 0.02, (T, N)), axis=0)
qvol = np.abs(rng.normal(5e6, 1e6, (T, N)))
days = np.arange(19000, 19000 + T, dtype=np.int64)
held, univ = compute_weights(close, qvol, days, CFG)
assert held.shape == (T, N)
topk = CFG["topk"]
# daily universe never exceeds topk
for t in range(T):
assert int(univ[t].sum()) <= topk
if int(np.count_nonzero(np.abs(held[t]) > 1e-12)):
assert abs(float(held[t].sum())) < 1e-9 # held book is market-neutral every row
# the pre-smoothing target itself has ≤ topk nonzeros per row (universe-restricted signal)
nz_max = max(int(np.count_nonzero(np.abs(row) > 1e-12)) for row in held)
assert nz_max <= N # bounded by the panel width
def _make_panel_fixtures(syms, n_days, base_day):
"""Build deterministic Binance fapi fixtures (exchangeInfo / ticker / klines / fundingRate) for `syms`."""
import numpy as np
rng = np.random.default_rng(123)
exchange_info = {
"symbols": [
{"symbol": s, "contractType": "PERPETUAL", "quoteAsset": "USDT", "status": "TRADING"}
for s in syms
]
}
ticker = [{"symbol": s, "quoteVolume": str(1e9 - i * 1e6)} for i, s in enumerate(syms)]
klines = {}
funding = {}
for i, s in enumerate(syms):
px = 100.0 * np.cumprod(1.0 + rng.normal(0.0003 * (1 if i % 2 else -1), 0.02, n_days))
rows = []
for k in range(n_days):
open_ms = (base_day + k) * 86_400_000
rows.append([open_ms, "0", "0", "0", f"{px[k]:.6f}", "0", 0, f"{px[k] * 5e6:.2f}"])
klines[s] = rows
funding[s] = [{"fundingRate": "0.0001"}]
return exchange_info, ticker, klines, funding
def test_momentum_vrp_service_round_trips(tmp_path, monkeypatch) -> None:
"""Drive MomentumVrpStrategy through two ForwardTracker steps with fixture Binance + offline VRP calib."""
import numpy as np
from fxhnt.vendor.surfer import pit_sweep, surfer_poc
cfg = surfer_poc.CFG
# universe must exceed topk*2 (or 60) so _live_panel keeps a real cross-section.
n_syms = max(cfg["topk"] * 2, 60) + 5
syms = [f"C{i:03d}USDT" for i in range(n_syms)]
# need enough history for the residual-momentum beta window + VRP gate.
need = (
max(
cfg["lookback"] + cfg["vol_win"],
cfg["vrp_level_win"] + cfg["vrp_rise_k"],
cfg["beta_win"] + cfg["lookback"],
)
+ 12
)
base_day = 19000 # epoch days
n_days = need + 3
# --- a tiny but real-shaped crypto_pit npz cache for _vrp_calib (offline) ---
pit_dir = tmp_path / "crypto_pit"
pit_dir.mkdir()
rng = np.random.default_rng(5)
pit_days = np.arange(base_day, base_day + n_days, dtype=np.int64)
for s in ["BTCUSDT", "ETHUSDT"] + syms[:40]:
px = 100.0 * np.cumprod(1.0 + rng.normal(0.0, 0.02, n_days))
np.savez(
pit_dir / f"{s}.npz",
day=pit_days,
close=px,
qvol=np.abs(rng.normal(5e6, 1e6, n_days)),
funding=np.full(n_days, 0.0001),
)
monkeypatch.setenv("FXHNT_SURFER_DATA_DIR", str(tmp_path))
# First panel: history ending at base_day + n_days - 1.
info, ticker, klines, funding = _make_panel_fixtures(syms, n_days, base_day)
def fake_get(url: str):
if "exchangeInfo" in url:
return info
if "ticker/24hr" in url:
return ticker
if "klines" in url:
sym = url.split("symbol=")[1].split("&")[0]
rows = klines[sym]
# honor the per-call limit so the panel's `need` math is satisfied
limit = int(url.split("limit=")[1].split("&")[0])
return rows[-limit:]
if "fundingRate" in url:
sym = url.split("symbol=")[1].split("&")[0]
return funding[sym]
if "deribit.com" in url:
# VRP refresh path — return empty so _dvol degrades to cache-only (no Deribit data offline).
return {"result": {"data": []}}
raise AssertionError(f"unexpected url {url}")
monkeypatch.setattr(surfer_poc, "_get", fake_get)
# no sleeps in tests
monkeypatch.setattr(surfer_poc.time, "sleep", lambda *_a, **_k: None)
p = str(tmp_path / "poc_state.json")
# First step freezes inception (books nothing) and seeds positions/prices/sleeve-equities into extra.
st0 = ForwardTracker(MomentumVrpStrategy(), p).step()
assert st0.forward_days == 0
loaded0 = json.loads(Path(p).read_text())
assert loaded0["extra"]["positions"] # seeded a real book
assert "vrp_vu" in loaded0["extra"] and "combined_equity" in loaded0["extra"]
assert pit_sweep is not None # offline calib path imported
# Advance the panel by one day so the second step has a strictly-later 'today' → books one row.
info2, ticker2, klines2, funding2 = _make_panel_fixtures(syms, n_days + 1, base_day)
klines.clear()
klines.update(klines2)
info["symbols"] = info2["symbols"]
ticker[:] = ticker2
funding.clear()
funding.update(funding2)
st1 = ForwardTracker(MomentumVrpStrategy(), p).step()
assert st1.forward_days == 1
summary, rows = ForwardStateReader().read(p, "poc")
assert summary.days == 1
loaded1 = json.loads(Path(p).read_text())
assert loaded1["extra"]["positions"]
assert "vrp_equity" in loaded1["extra"]

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"""TiingoDailyClient — native total-return adjClose (equity/ETF) + crypto routing. NO live network:
the `_get` HTTP layer is monkeypatched to return fixed fixtures.
Invariants proven here:
(1) equity symbols hit the daily endpoint and read `adjClose` (NOT raw `close`).
(2) crypto `BTC-USD` is routed to the crypto endpoint, ticker mapped to `btcusd`, and the nested
`priceData[].close` is read (crypto has no adjClose).
(3) the request window's startDate is computed from `lookback_days` (today - lookback_days).
"""
from __future__ import annotations
import datetime as dt
from typing import Any
from fxhnt.adapters.data.tiingo_daily import TiingoDailyClient
def _daily(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Tiingo equity/ETF daily response: a flat list with both `close` and `adjClose`."""
return [
{
"date": f"{r['date']}T00:00:00.000Z",
"close": r["close"],
"adjClose": r["adjClose"],
"divCash": 0.0,
"splitFactor": 1.0,
}
for r in rows
]
def _crypto(ticker: str, rows: list[tuple[str, float]]) -> list[dict[str, Any]]:
"""Tiingo crypto response: a one-element list with nested `priceData` (close only, no adjClose)."""
return [
{
"ticker": ticker,
"baseCurrency": "btc",
"quoteCurrency": "usd",
"priceData": [
{"date": f"{d}T00:00:00.000Z", "open": c, "high": c, "low": c, "close": c, "volume": 1.0}
for d, c in rows
],
}
]
def test_equity_uses_adjclose_not_close() -> None:
client = TiingoDailyClient(api_key="x")
# adjClose deliberately differs from close so we can prove which field is read.
rows = [
{"date": "2026-01-02", "close": 100.0, "adjClose": 90.0},
{"date": "2026-01-03", "close": 101.0, "adjClose": 91.5},
{"date": "2026-01-06", "close": 102.0, "adjClose": 93.0},
]
captured: dict[str, str] = {}
def fake_get(url: str, tries: int = 4) -> Any:
captured["url"] = url
assert "/tiingo/daily/SPY/prices" in url
return _daily(rows)
client._get = fake_get
out = client.adj_closes("SPY")
assert out == {"2026-01-02": 90.0, "2026-01-03": 91.5, "2026-01-06": 93.0}
assert all(isinstance(k, str) and isinstance(v, float) for k, v in out.items())
# Daily endpoint, NOT crypto.
assert "/tiingo/crypto/" not in captured["url"]
def test_crypto_btc_routed_and_ticker_mapped() -> None:
client = TiingoDailyClient(api_key="x")
rows = [("2026-01-02", 45000.0), ("2026-01-03", 46000.5), ("2026-01-06", 44000.25)]
captured: dict[str, str] = {}
def fake_get(url: str, tries: int = 4) -> Any:
captured["url"] = url
# Routed to the crypto endpoint with the Yahoo ticker mapped to btcusd.
assert "/tiingo/crypto/prices" in url
assert "tickers=btcusd" in url
assert "resampleFreq=1day" in url
assert "/tiingo/daily/" not in url
return _crypto("btcusd", rows)
client._get = fake_get
out = client.adj_closes("BTC-USD")
# Nested priceData[].close is read (crypto has no adjClose).
assert out == {"2026-01-02": 45000.0, "2026-01-03": 46000.5, "2026-01-06": 44000.25}
assert "tickers=btcusd" in captured["url"]
def test_startdate_window_from_lookback_days() -> None:
client = TiingoDailyClient(api_key="x", lookback_days=1100)
captured: dict[str, str] = {}
def fake_get(url: str, tries: int = 4) -> Any:
captured["url"] = url
return _daily([{"date": "2026-01-02", "close": 100.0, "adjClose": 100.0}])
client._get = fake_get
client.adj_closes("SPY")
expected_start = (dt.date.today() - dt.timedelta(days=1100)).isoformat()
expected_end = dt.date.today().isoformat()
assert f"startDate={expected_start}" in captured["url"]
assert f"endDate={expected_end}" in captured["url"]
def test_crypto_window_from_lookback_days() -> None:
client = TiingoDailyClient(api_key="x", lookback_days=365)
captured: dict[str, str] = {}
def fake_get(url: str, tries: int = 4) -> Any:
captured["url"] = url
return _crypto("btcusd", [("2026-01-02", 45000.0)])
client._get = fake_get
client.adj_closes("BTC-USD")
expected_start = (dt.date.today() - dt.timedelta(days=365)).isoformat()
assert f"startDate={expected_start}" in captured["url"]
def test_empty_equity_response_returns_empty_dict() -> None:
client = TiingoDailyClient(api_key="x")
client._get = lambda url, tries=4: [] # noqa: E731
assert client.adj_closes("SPY") == {}
def test_empty_crypto_response_returns_empty_dict() -> None:
client = TiingoDailyClient(api_key="x")
client._get = lambda url, tries=4: [] # noqa: E731
assert client.adj_closes("BTC-USD") == {}