# Australian Gallops Trifecta Predictor

A production-quality, transparent trifecta prediction system for **Australian
gallops**, powered by the **FormFav API**. It discovers tomorrow's meetings,
fetches fields + form for every runner, scores each runner with a configurable
weighted model, ranks them, and emits 1st/2nd/3rd trifecta combinations with
confidence and explainable reasoning — never a guarantee.

> Predictions are statistical estimates based on available data. They are **not**
> guarantees of any racing outcome or betting success.

## Architecture

```
trifecta_bro/
  api/            FormFavClient (X-API-Key, retry/backoff, cache), response validator
  data/           pydantic models, normalizer (scratch/abandon/missing), SQLite storage
  model/          feature_engine (form-quality + x/spell), scoring (configurable),
                  pace_analysis, probability (trifecta + confidence)
  reporting/      Markdown dashboard + JSON exporter
  jobs/           daily_prediction orchestrator (python -m ...)
  evaluation/     backtester + metrics (no data leakage)
  tools.py        Hermes/Agent reusable tools
  config/         settings.py (env-driven), weights.yaml (configurable weights)
tests/            unittest suite (mocked FormFav, no network)
```

The **LLM/agent is never the prediction engine**. All scoring, ranking,
probabilities, confidence and trifecta generation are deterministic statistics
so results are reproducible. The agent layer is for orchestration, explanation,
and querying.

## Setup

```bash
cd trifecta-bro-hf-space
python3 -m venv .venv && . .venv/bin/activate
pip install -r requirements.txt   # httpx, pydantic, pyyaml, reportlab, ...
cp .env.example .env              # then edit .env and paste your key
```

Configure the key (server-side only — never in source, logs, or reports):

```
FORMFAV_API_KEY=your_key_here
```

All other settings (country, race_code, timezone, cache TTL, DB path, weights
path, model version) have sensible defaults and are overridable via env vars.

## Run tomorrow's analysis (live)

```bash
python -m trifecta_bro.jobs.daily_prediction
# or a specific date:
python -m trifecta_bro.jobs.daily_prediction --date 2026-08-11
```

This will:
1. Resolve tomorrow's date in `Australia/Sydney`.
2. `GET /form/meetings?date=...&country=au&race_code=gallops`.
3. For each AU, non-abandoned meeting, fetch every race via `GET /form`.
4. Normalise, drop scratched runners, skip abandoned races.
5. Score + rank every runner, generate trifecta + savers + confidence.
6. Persist to SQLite (`data/trifecta_bro.db`).
7. Write `data/reports/predictions-<date>.json` and `report-<date>.md`.

## Run without a key (offline / CI)

The prediction model runs on any race-form payload — no API needed for analysis,
tests, or backtesting. Try the end-to-end demo (uses bundled mock data):

```bash
python run_e2e_demo.py
```

## Backtesting

```bash
python -m trifecta_bro.evaluation.backtester --date 2026-08-10 \
        --actuals actuals.json
```

`actuals.json` maps `"track_slug:RACE"` -> `"1,7,9"`. Metrics: Top-1 accuracy,
place accuracy, trifecta box hit rate, exact trifecta hit rate, average partial
coverage, average confidence, and per-race records. The model is evaluated only
on pre-race fields — the actual result is never fed back into scoring, so there
is **no data leakage**.

## Tests

```bash
python -m unittest discover -s tests -v
```

Covers: date logic, meetings retrieval, race retrieval, API-error handling,
scratched runners, abandoned races, missing fields, form-string parsing,
x/spell parsing, runner scoring, pace scoring, barrier scoring, ranking,
trifecta generation, confidence, DB persistence, backtesting, and a full
mocked end-to-end pipeline.

## Hermes / Agent tools

`trifecta_bro/tools.py` exposes: `get_tomorrow_meetings`, `get_race_form`,
`analyse_race`, `rank_runners`, `generate_trifecta`, `save_prediction`,
`run_daily_analysis`, `why_ranked_first`. An agent can execute a request like
"Run tomorrow's Australian gallops analysis" or "Why did you rank runner 4
first?" entirely through these deterministic wrappers.

## Tuning the model

Edit `trifecta_bro/config/weights.yaml` — weights are normalised at load time, so
the sum need not be exact. Weights are deliberately a **starting point**, not a
claimed optimum; tune them against historical prediction/result data via the
backtester, and add ML models (logistic regression, random forest, GBM) behind
the same `analyse_race` interface for out-of-sample comparison.

## Limitations (FormFav tier / data)

- `/predictions` (premium) returns 403 on basic keys; the system gracefully
  falls back to the local transparent model only.
- Base-tier form data lacks an explicit jockey/trainer *strike-rate* field; the
  model uses career win%/place% as a transparent proxy and flags it as
  lower-confidence input.
- Non-AU meetings (e.g. NZ jumps) appear in `/form/meetings`; the code filters
  strictly on `country == au`.
- Where FormFav supplies no pace scenario, a derived pressure proxy is used; the
  pace label is therefore an estimate, not a measured value.