PulseQuant Dashboard
Python Pandas Engine via Pyodide WASM
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Loading Python environment with Pandas, NumPy, and SciPy
Trading Strategy Overview
1. Micro-Price (The Foundation)
Instead of using a naive Mid-Price, the engine uses Micro-Price, which weights the price toward the side with the most volume. This allows indicators to react to liquidity shifts before the actual spread crosses.
2. Normalized OFI EMA & Macro Trend (Momentum)
Triggers aggressive taker market orders when normalized Order Flow Imbalance (OFI) EMA exceeds thresholds, aligned with the macro trend (Micro-Price vs Macro SMA). Normalizes OFI using Z-scores to adapt to changing volatility.
3. VWAP & Normalized OBI (Mean Reversion)
Buys on deep mean reversion (negative OBI Z-score indicating heavy sell pressure + VWAP discount) combined with OFI absorption. Similarly, shorts on positive OBI Z-scores showing heavy buy pressure and a VWAP premium when OFI shows exhaustion.
4. Bollinger Bands (Volatility & Bounds)
Computes organic volatility using Bollinger Bands around the Micro-Price. These upper, lower, and mid bands act as dynamic thresholds alongside trailing VWAP/SMA boundaries to visualize abnormal price excursions and compressions.
5. Trend Continuation & Cooldowns
Buys minor dips (VWAP discount) in a strong macro uptrend (positive Macro SMA slope) when OFI flips positive. Sells minor rips in strong downtrends. Each execution locks the strategy for 1s to 5s based on strategy style to prevent signal over-firing.
6. Risk Management & Sizing
Auto-trade dynamically adjusts static position sizes (50 to 250 bps) based on the Strategy Style selected above, overriding manual size. It enforces strict risk parameters, automatically closing positions with taker requests if Micro-Price moves 5% against entry.