Verdict Edge — Edge Mining Framework

A small library for prediction-market signals. It evaluates compound feature rules, then approves a trade only if the expected value remains positive after exchange fees. It also reports a Kelly-sized position. The package is verdict-edge; the import is edge_mining_framework.

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What ships today

A small, dependency-light library (numpy only) with two stages and a receipt helper:

features (dict) ──▶ FeatureEvaluator.evaluate_compound(rules) ──▶ ExpectedValueGate ──▶ trade / no trade
                                                                        │
                                                   build_strategy_receipt (Verdict provider receipt)

Component	What it does	Source
FeatureEvaluator	AND-only compound rules with fail-fast short-circuit. Scalar operators == != > < >= <= in_range in_set; series operators crosses_above crosses_below zscore rolling_corr rank. No eval().	src/edge_mining_framework/evaluator.py
ExpectedValueGate	Fee-aware expected value per contract, Kelly fraction, and recommended size for binary prediction-market contracts.	src/edge_mining_framework/gate.py
build_strategy_receipt / canonical_hash	Builds a Verdict provider receipt (ADR-021 shape). Conformance against verdict-core is tested in tests/test_core_conformance.py.	src/edge_mining_framework/provider_receipts.py

The evaluator is data-source agnostic. It does not ingest market feeds, place orders, or call exchange APIs; those belong to the caller. Risk limits live in verdict-risk, and Monte Carlo validation lives in verdict-backtest.

Principles

1. Deterministic: the same inputs give the same outputs.
2. Friction-aware: the EV gate subtracts the exchange fee before it approves a trade.
3. Agnostic: rules evaluate plain dictionaries. There are no exchange SDK imports.

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Quick start

The package is not published to PyPI. Install from source:

git clone https://github.com/mrnicholasbcarter-code/verdict-strategy.git
cd verdict-strategy
uv sync --extra dev
uv run python examples/gate_kalshi.py      # EV gate rejects / accepts two trades
uv run python examples/run_evaluator.py    # rules from examples/rules.yaml through both stages
uv run pytest -q

from edge_mining_framework import ExpectedValueGate, FeatureEvaluator

features = {"rsi": 28.0, "vol_expansion": True}
rules = [
    {"feature": "rsi", "operator": "<", "threshold": 30},
    {"feature": "vol_expansion", "operator": "==", "threshold": True},
]

if FeatureEvaluator.evaluate_compound(features, rules):
    metrics = ExpectedValueGate.calculate_ev_metrics(
        predicted_win_prob=0.57,
        current_contract_price_cents=48,
        payout_cents=100,
        exchange_fee_pct=0.04,
        bankroll=500.0,
    )
    print(metrics.profitable, round(metrics.expected_value, 2), round(metrics.kelly_fraction, 4))

examples/rules.yaml shows the rule format, including series operators. examples/backtest_adapter.py shows the optional hand-off to an installed verdict-backtest.

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Links

• Verdict Core: https://github.com/mrnicholasbcarter-code/verdict-core
• Verdict Risk: https://github.com/mrnicholasbcarter-code/verdict-risk
• Verdict Backtest: https://github.com/mrnicholasbcarter-code/verdict-backtest

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License

MIT — see LICENSE