nakagai

The deterministic, LLM-free core for rule-driven trading agents: a point-in-time bar cache, a statistically honest walk-forward backtester (look-ahead prevention, T+1 cash settlement), the RuleSpec strategy DSL, and a screener compiler.

What is here

• data/: BarCache/MemoryBars over local parquet, the DataProvider contract and its Alpaca implementation (single-symbol and batched multi-symbol), and a sync routine that keeps the cache current.
• engine/: the walk-forward backtester itself, point-in-time MarketContext assembly, T+1 cash settlement, and run metrics.
• strategies/: rule-based (rules/), boolean-composed (composite/), and ICT-flavored (ict/) strategies, plus a catalog loader that turns JSON specs into strategy classes.
• screen/: a conditions-only screener over the same RuleSpec grammar. Evaluation is deterministic and LLM-free; an optional English-to-spec compiler shares the nlbuilder extra with nlbuilder/, which installs anthropic.
• nlbuilder/: English-to-RuleSpec compilation via the Claude API, behind the optional nlbuilder extra (installs anthropic).
• stats.py: poolable return moments and the deflated-Sharpe family (PSR, DSR, minimum track record length, effective trial count), which is how a candidate is priced for how many candidates were tried.
• icir.py: rank-IC / IR of rule-spec margins vs forward returns (the informational ICIR lens).
• filelock.py: cross-process advisory file locking for concurrent read-modify-write on shared result files.

Quickstart

This builds a BarCache, loads one of the shipped example strategies, runs the walk-forward engine over the cached window, and prints run metrics next to buy-and-hold. No network, no credentials, no optional extras, and it prints the same numbers every time: the engine's whole contract is that a backtest reads the cache and nothing else. Run it from the repo root with uv run python quickstart.py (or paste it into a REPL):

import tempfile
from pathlib import Path

import numpy as np
import pandas as pd

from nakagai.data.cache import BarCache
from nakagai.data.schema import TimeframeSet, validate_bars
from nakagai.engine.engine import Engine
from nakagai.engine.metrics import buy_and_hold_return, summarize
from nakagai.strategies.catalog import load_catalog
from nakagai.strategies.rules import core_vocabulary

# 1. Generate a deterministic hourly series. Swap this block for
#    AlpacaProvider().fetch_bars("SPY", "1h", start, end) once you have
#    ALPACA_KEY_ID / ALPACA_SECRET_KEY; everything below is unchanged, which is
#    the point of the DataProvider seam.
rng = np.random.default_rng(0)
idx = pd.date_range("2024-01-01", periods=2000, freq="1h", tz="UTC", name="ts")
close = pd.Series(400 * np.exp(np.cumsum(rng.normal(0, 0.006, len(idx)))), index=idx)
prev = close.shift(1).fillna(close.iloc[0])
bars = validate_bars(pd.DataFrame({
    "open": prev,
    "high": np.maximum(close, prev) * 1.004,
    "low": np.minimum(close, prev) * 0.996,
    "close": close,
    "volume": 1_000_000.0,
}, index=idx))

# 2. Store it in a local BarCache: parquet on disk, offline after this.
cache = BarCache(Path(tempfile.mkdtemp()))
cache.upsert("SPY", "1h", bars)

# 3. Load a shipped example strategy from the catalog.
specs_dir = Path("nakagai/strategies/catalog/specs")
catalog = load_catalog(specs_dir, core_vocabulary)
strategy = catalog["sma_cross"]({})

# 4. Run the engine over the cached window.
tfs = TimeframeSet(driving="1h", deltas={"1h": pd.Timedelta(hours=1)})
engine = Engine(strategy, cache, "SPY", bars.index[0], bars.index[-1], tfs=tfs)
result = engine.run()

# 5. Print metrics next to buy-and-hold.
bh = buy_and_hold_return(bars, bars.index[0], bars.index[-1])
metrics = summarize(result, bh_return=bh)
print(f"trades: {metrics['n_trades']}, win_rate: {metrics['win_rate']:.2f}, "
      f"profit_factor: {metrics['profit_factor']:.2f}, total_return: {metrics['total_return']:.2%}, "
      f"bh_return: {metrics['bh_return']:.2%}")

Because the series is seeded, this prints the same line on every machine, which makes it a usable smoke test as well as an example:

trades: 25, win_rate: 0.32, profit_factor: 0.92, total_return: -1.27%, bh_return: -28.77%

A trend follower run on a random walk is not supposed to make money, and it doesn't. That is the example working, not failing: the engine's job is to tell you that honestly. Point step 1 at real bars to see something worth judging.

Two details of the generated series matter if you change it. Position size comes from risk_pct divided by the ATR stop distance, so a series with a low price-to-volatility ratio asks for more shares than equity0 can buy and every entry is skipped, which reads as a silent zero-trade run. And the bars are continuous hourly, with no session gaps, which is fine for the 1h driving timeframe here but is not what session-aligned daily logic expects.

The RuleSpec DSL

A RuleSpec is plain JSON: an entry condition tree for long and short, and a risk block for the stop and target. Conditions compare an indicator or price source against another indicator or a constant, with operators like crosses_above and crosses_below; all/any groups combine them into arbitrarily nested boolean trees. nakagai.strategies.rules.validate_spec is the single source of truth for the grammar, so a spec that loads has already been checked. Here is the shipped sma_cross.json example, abridged to the DSL fields (catalog card metadata like category and tags omitted):

{
  "title": "Moving average crossover",
  "description": "The classic trend follower: long when the fast SMA crosses above the slow SMA on the 1h chart, short on the cross down. ATR-sized stop, fixed reward:risk target.",
  "spec": {
    "version": 2,
    "name": "sma_cross",
    "timeframe": "1h",
    "long": {"all": [
      {"lhs": {"ind": "sma", "n": 20}, "op": "crosses_above", "rhs": {"ind": "sma", "n": 50}}
    ]},
    "short": {"all": [
      {"lhs": {"ind": "sma", "n": 20}, "op": "crosses_below", "rhs": {"ind": "sma", "n": 50}}
    ]},
    "risk": {"stop": {"kind": "atr", "n": 14, "mult": 2.0}, "target": {"kind": "rr", "rr": 2.0}}
  }
}

Two more examples ship in nakagai/strategies/catalog/specs/: rsi_reversion.json (mean reversion) and macd_trend.json (momentum). load_catalog(specs_dir, core_vocabulary) turns every JSON file in a directory like this one into a RuleStrategy subclass.

What is NOT here

This repo does not include the curated Playbook content (the hand-authored strategy specs), the evidence store and proving pipeline, the intraday scanner, or the hosted platform: API, web UI, and the mandate and approvals judgment layer. The hosted product at nakag.ai is built on top of this core.

Release notes

0.4.1

• Canonicalize daily cache rows to midnight New York by UTC session date, so mixed provider labels cannot retain duplicate rows for one market session.

0.4.0

Breaking: nakagai.stats.pf_from_trades and PF_CLAMP are removed. They computed a pooled profit factor over a trade ledger. The lab was their only caller, and with the lab gone in 0.3.0 nothing reached them: not core, not the hosted platform, which derives profit factor from its own gross sums. The module no longer imports pandas.

Breaking: run_one loses its icir keyword. It opted a caller out of the ICIR lens, and it had exactly two callers, the permutation harness and the frontier open-window snapshots. Both were retired, so the flag has been dead in production for some time and only a test still set it. The lens itself is untouched and still runs for rule specs, still abstains to empty fields for everything else, and still degrades to empty rather than killing a run row.

Breaking: Engine.slippage_for is removed. A one-line accessor over SlippageModel.per_share, added so callers could ask the engine what it would charge without reaching into the model. No caller ever did. Its only reference was a test asserting the method exists, which is a test that cannot fail for any reason worth catching, so it went too.

This release is also the first to carry everything merged since 0.3.0, which shipped without a version bump: the deflated-Sharpe family, the injected vocabulary reaching composites, and the session-open fix.

0.3.0

Breaking: nakagai.lab is removed. The module searched strategy space and scored the winner against a best-of-N permutation null. It shipped in 0.2.0 with one consumer, the hosted platform's study subsystem, and that subsystem was retired; nothing has imported the lab since. Gone with it: Site, Trial, composite_trials, literal_trials, mutable_sites, spec_hash, best_of_n_null, study_verdict, StudyResult, StudySpec, TrialResult, run_study, trial_pf, and the Calibration workflow that gated them.

Note that spec_hash also exists, unrelated and unaffected, at nakagai.strategies.rules.canon.spec_hash. Only the lab's is gone.

Breaking: the bar-permutation Monte Carlo null is removed with it. Gone: nakagai.engine.permutation entirely (permute_bars, permutation_seed) and nakagai.stats.permutation_pvalue. These were the two halves of one feature, generating null price series and scoring an observation against them, and the lab was the only thing that ever called either. Permutation testing is no longer part of what this core does.

nakagai.stats keeps pf_from_trades and PF_CLAMP, and its module docstring no longer describes it as permutation-test math.

The question the lab answered, "is this survivor real or did I just search hard enough to find noise", is not being abandoned; it is moving to the deflated-Sharpe family, which prices the same overfitting risk from the trial count directly rather than by replaying the search on permuted bars.

0.2.0

Behavior change: every session-scoped term is anchored on the 09:30 bell and scoped to regular hours. Backtest output moves for any play reading opening_range_high, opening_range_low, minutes_into_session, prev_session_high, prev_session_low, prev_session_close, gap_pct or vwap; re-run anything that depends on them. The bar caches are not regular-hours-only, and these all grouped a New York calendar date and treated its first row as the session's start, which is ordinarily an 08:00 pre-market print. So the opening range was a thin band nobody trades, minutes_into_session ran an hour and a half fast, the previous session's high and low were off-hours extremes and its "close" was the last post-market print, a gap was measured from 19:45 to 08:00, and session VWAP was set by pre-market volume. A session now runs [09:30, 16:00) on the exchange wall clock, from nakagai/data/schema.py, and a bar before the bell reads NaN rather than a value a condition would act on. A daily frame is unaffected: one row is its own whole session.

Behavior change: day_of_week reads the weekday off the FRAME, not off a label's clock. Backtest output moves for any play using day_of_week on an intraday frame; re-run anything that depends on it. The old predicate decided which clock to read by looking for a midnight-UTC label, and the bar caches are not regular-hours-only, so a 19:00 New York post-market bar carries exactly the label a resampled daily bar carries and was read as the next day: Tuesday, for a Monday evening. It answered wrong on one bar of a session and right on all the others, which is the shape of divergence a spec author never catches. The weekday is now the frame's to decide, per strategies/rules/primitives.py.

New: a Pine v6 compiler for RuleSpec v2. compile_pine(spec, vocabulary) returns an indicator and a strategy, rendered from one lowering so the pair cannot disagree about which bar decided; lower_pine returns the target-neutral program underneath. Both are exported from nakagai.strategies.rules, alongside PineBundle and PineCompileError. Every export charts the engine's 15-minute driving cadence and requests a play's own timeframe rather than charting it, so the script refuses any other chart at runtime, and it requires extended trading hours for the same reason the engine's own frames carry pre-market bars.

Breaking: the catalog loaders require a vocabulary factory. load_catalog(specs_dir) becomes load_catalog(specs_dir, core_vocabulary), and the same for load_entries. Both are cached on their whole argument tuple, so a defaulted call and an explicit one built two different strategy classes over the same spec files, with isinstance quietly disagreeing and nothing raising.

Development

uv sync --all-extras
uv run pytest

uv sync --all-extras pulls in anthropic so the nlbuilder tests run too; the rest of the package works fine without it.

License

MIT