BNF Signal is a quantitative market-observation engine designed to identify extreme mean-reversion events. It is a technical tribute to Takashi Kotegawa (alias: BNF), the legendary Japanese trader who mastered the art of buying market capitulation.
The engine monitors the divergence between a security's spot price and its 25-day Simple Moving Average (SMA). This specific metric was the cornerstone of BNF’s early success in the Japanese markets.
A signal is triggered when the price-to-SMA deviation of a stock meets the following condition:
- Capitulation Detection: Identifying points where retail panic creates an "overstretched" rubber band effect.
- High Liquidity Focus: By default, the scanner targets S&P 500 constituents to ensure efficient exits.
- Mean Reversion: Anticipating a violent "snap back" toward the 25-day average once the selling pressure is exhausted.
- Real-time Scanning: Fetches the most recent daily closes for the S&P 500.
- Automated Ticker Ingestion: Dynamically updates the watch list via Wikipedia's S&P 500 records.
- Telegram Integration: Delivers actionable telemetry directly to your mobile device.
- Batch Processing: Optimized
yfinancecalls to prevent IP rate-limiting.
git clone [https://github.com/yourusername/bnf-signal.git](https://github.com/yourusername/bnf-signal.git)
cd bnf-signalpip install yfinance pandas requests
Create a bot via @BotFather to get your API_TOKEN.
Get your CHAT_ID from @userinfobot.
Update the configuration section in bnf_signal.py.