kalshi_edge

Research + trading CLI for Kalshi BTC Above/Below ladders. It compares ladder prices (YES/NO binaries, quoted in cents) to a simple model probability $p_{\text{model}}=\mathbb{P}(S_T \ge K)$ derived from deeper BTC venues (spot + volatility), and reports execution-aware EV per contract.

Disclaimer: research/education only — not financial advice. These markets are risky: model error, fees, and liquidity can dominate.

Quick start

From the repo root:

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -r requirements.txt

# run a single evaluation snapshot
python3 -m kalshi_edge --config strategy_config.example.json

# watch mode
python3 -m kalshi_edge --config strategy_config.example.json --watch

# paper trader (no real orders)
python3 -m kalshi_edge --config strategy_config.example.json --watch --trade --dry-run

Docs

• docs/guide.md: how to run the project + how config works
• docs/model.md: probability + EV model (p_model, sigma_blend, edge_pp)
• docs/paper_trading.md: paper trading (--dry-run) and maker-fill simulation notes
• docs/backtest.md: minute-cadence backtesting with 1-minute candlesticks