Implementation Plan - Vector-HaSH Financial Trader

Objective

Implement the Vector-HaSH algorithm for predicting pure financial prices (XAUUSD 3-minute timeframe) inside Google Colab (T4 GPU). Evaluate strategy via strict anchored Walk-Forward Optimization (WFO) to eliminate forward-looking bias.

Proposed Strategy Architecture

1. Feature Engineering

We will rely ONLY on pure price transformations.

Compute rolling features: Log returns, rolling volatility, and sequence windows of size $W$ (e.g. 15 bars). Let the state at time $t$ be $\mathbf{x}_t \in \mathbb{R}^{W}$.
Discrete Quantization: To map continuous prices into the discrete elements similar to the visual "sbook" in Vector-HaSH, we will use flash-kmeans (with $K$ clusters) to quantize the historical $\mathbf{x}_t$ vectors into discrete sensory classes $\mathbf{s}_t$.

2. Vector-HaSH Memory Scaffold

Instead of a 2D spatial grid, we will use a 1D Continuous Track (approximating time).

Grid Scaffold ($\mathbf{g}_t$): Synthesize multiscale 1D grid cell representations (using sine/cosine waves or cyclic shifts).
Place Cells ($\mathbf{p}_t$): Project Grid cells into a sparse higher-dimensional space: $\mathbf{p}t = \sigma(\mathbf{W}{pg} \mathbf{g}_t)$.
Hetero-associative Memory: Train the sensory-to-place map $\mathbf{W}_{sp}$ dynamically using Recursive Least Squares (RLS), mimicking the pseudotrain_2d_iterative_step seen in MTT.py.

3. Machine Learning Wrapper (XGBoost)

At time $t$, extract the Memory Recall Error ($\mathbf{s}_t - \hat{\mathbf{s}}_t$) and the Place Cell Activations ($\mathbf{p}_t$).
Feed these VectorHaSH embeddings into an XGBoost Classifier/Regressor.
Target: Next bar log return $r_{t+1}$ or direction $\text{sign}(r_{t+1})$.

4. Anchored Walk-Forward Optimization

To avoid cheating:

Train/Test splits expand over time.
Fold 1: Train $[0, T]$, Test $[T, T+H]$.
Fold 2: Train $[0, T+H]$, Test $[T+H, T+2H]$.
flash-kmeans, Vector-HaSH memory construction, and XGBoost fitting will occur ONLY on the Training slice of each fold, and act out-of-sample on the Test slice.

5. Mono-Script Colab Implementation (`vector_hash_trader.py`)

Vectorized using PyTorch (device='cuda') or NumPy (cuml/cupy/XGBoost-GPU).
Plotting module included: cumulative returns, drawdown, WFO heatmaps, and memory collapse analysis.

Verification

Assert strictly positive index lookups when indexing arrays (no t to t+1 leakage before target definition).
Verify standard performance metrics: Sharpe Ratio, Sortino Ratio, Max Drawdown.