Binance Signal Engine

Multi-timeframe crypto technical analysis signal generator for OpenClaw / ClawHub.

What It Does

Binance Signal Engine layers three timeframes (1D / 4H / 15m) into a single weighted technical analysis score for any Binance-listed crypto pair. When conditions align, it generates a complete trade plan with entry, stop-loss, take-profit, and position sizing.

═══════════════════════════════════════════════════
  BTC/USDT  |  SPOT  |  Score: 37.0
═══════════════════════════════════════════════════
  Regime : bullish
  Bias   : STRONG BULLISH
  Action : BUY
  Trend  : +25.0  |  Momentum: +7.0  |  Trigger: +5.0

  Signal Reasons:
    • Price > EMA50 (1D)
    • EMA9 > EMA21 (1D)
    • ADX=32.4 strong bullish trend
    • MACD > Signal (4H)
    • MACD histogram rising (4H)
    • RSI reclaimed above oversold (38.2)
    ...

  Trade Plan (READY):
    Side       : long
    Entry_type : market
    Entry      : 97432.10
    Stop_loss  : 95891.30
    Take_profit: 100514.70
    Effective_risk_reward: 2.00

  Position Size:
    Units      : 0.00648
    Notional   : $631.48
    Risk Budget: $100.00
═══════════════════════════════════════════════════

Installation

Via ClawHub (recommended)

npx clawhub@latest install binance-signal-engine

Manual

# Clone into your OpenClaw skills directory
git clone https://github.com/eplt/binance-signal-engine.git \
  ~/.openclaw/skills/binance-signal-engine

# Install Python dependencies
pip install ccxt pandas numpy ta

Usage

From OpenClaw Chat

Just ask naturally:

• "Analyze BTC/USDT"
• "What's the signal on ETH?"
• "Give me a trade setup for SOL/USDT on futures with 3x leverage"
• "Scan BTC, ETH, and SOL"

From the Command Line

# Basic analysis
python3 scripts/binance_signal_engine.py BTC/USDT

# Multiple pairs, JSON output
python3 scripts/binance_signal_engine.py BTC/USDT ETH/USDT SOL/USDT -o json

# Futures with custom parameters
python3 scripts/binance_signal_engine.py BTC/USDT -m futures -l 3 -b 5000 -r 2

Architecture

1D  (high)  →  Trend Regime    EMA 9/21/50, ADX, DI+/DI−
4H  (mid)   →  Momentum        MACD cross/histogram, Stochastic
15m (low)   →  Entry Trigger   RSI reclaim, BB re-entry, volume spike, divergence
                    ↓
              Weighted Score  →  Bias  →  Trade Plan  →  Position Size

Each layer contributes independently to a composite score. The trend regime contextualises how momentum and trigger signals are interpreted — a bullish stochastic cross from oversold carries more weight in a bullish regime than a bearish one.

Documentation

Full documentation is in references/guide.md. The guide covers configuration, indicators, scoring, trade plans, position sizing, and examples. Review it for detailed explanations and common scenarios.

Configuration

Create a JSON file and pass it with --config:

{
  "ema_fast": 9,
  "ema_slow": 21,
  "ema_trend": 50,
  "adx_trend_threshold": 25.0,
  "rsi_oversold": 35.0,
  "rsi_overbought": 65.0,
  "atr_sl_multiplier": 1.5,
  "risk_reward_ratio": 2.0,
  "account_balance": 10000.0,
  "account_risk_pct": 1.0
}

All 40+ parameters are documented in the Config dataclass in the source.

Security

• No API keys required — public OHLCV data only
• No orders placed — read-only exchange interaction
• No data exfiltration — all computation is local
• No files written to disk

Requirements

• Python 3.8+
• ccxt pandas numpy ta

License

MIT

Author

Edward Tsang — blockchain & AI engineer. Open to consulting → Email · LinkedIn