The same H₀ persistent homology that powers Apex17 robotics now fingerprints market regimes. Birth-death barcodes replace moving averages. O(1) recall replaces retraining. <1ms replaces "overnight batch."
Six deterministic stages from raw market data to live order execution. No human in the loop. No retraining. Every decision anchored to a regime hash.
Ingest price, volume, order book data via Polygon/Alpaca feeds
H₀ persistence on price series. Union-Find elder rule. Birth-death pairs.
Top-8 pairs quantized into 16-byte descriptor. O(1) exact-match hash.
20-dim cosine similarity match against RegimeMemory ring buffer.
Position size, R:R, drawdown, exposure, confidence — all enforced.
Alpaca REST API. Real broker. Real capital. Real accountability.
These are not projections. These are measured values from production code.
Pure Python chain-graph H₀ persistence. Same algorithm as Apex17's CUDA kernel, CPU-only since market data windows are tiny (100-1000 bars).
Sort bars by value (ascending filtration). Activate in order. Merge with temporally adjacent bars via Union-Find with elder rule. Extract birth-death pairs. Compute stability, entropy, regime hash.
Top-8 persistence pairs quantized into 64 bins, packed into a 16-byte descriptor, then SHA-256 hashed to a 16-char hex string. Result: O(1) exact-match recall — "I've seen this exact market before."
Stores fingerprints with time-decay weighting. Each regime becomes a 20-dimensional vector (spectral, volatility, flow, momentum). Cosine similarity recalls historical outcomes for identical market structures.
Every order passes through 8 configurable risk limits before execution. Position size, risk-reward ratio, confidence threshold, max drawdown, max exposure. Regime memory adjusts sizing via multiplier [0.7 — 1.3×].
Each regime has a name, win rate, average return, and recommended action. The system doesn't guess — it recognizes.
Every trade proposal passes through TitanRiskAgent validation before execution. No exceptions. No overrides.
Source: src/neural_chat/titan_risk_agent.py · RiskLimits dataclass · All values configurable per-strategy
Traditional quantitative stacks retrain models on regime shifts. SignalBrain-OS fingerprints the shift itself.
Structural identity — birth-death barcodes capture the shape of market structure, not statistical moments. No retraining on regime change.
Descriptive statistics — moving averages, Bollinger bands, RSI. All lag the market. Models trained on past regimes fail on new ones.
"If you can fingerprint a point cloud, you can fingerprint a price series. The math doesn't care if the input is LiDAR or OHLCV — topological identity is domain-agnostic."
Three properties that statistical approaches fundamentally cannot replicate.
Topology detects regime transitions before statistics do. When the persistence barcode changes shape, the market is shifting — even if moving averages haven't crossed yet.
Statistical models must be retrained when market structure changes. The topology engine adapts instantly — it computes structure from raw data on every tick. No training set, no overfitting.
The same compute_market_topology() function runs on price series, volatility curves, and flow data. Robotics, healthcare, defense — same algorithm, different sensor input.
Transparency is a moat. Every other company only talks about wins.
Every number traces to a source file. No black boxes.
# Compute topology on live price data from neural_chat.market_topology import compute_market_topology result = compute_market_topology(close_prices, volumes=volumes) # Output: stability: 0.847 ← regime holding strong entropy: 1.23 bits ← moderate complexity max_persistence: 0.0312 ← dominant feature num_components: 7 regime_hash: "0x4A7F2C1D8E3B" ← O(1) recall key # Source files: src/neural_chat/market_topology.py 318 lines H₀ persistence src/neural_chat/regime_memory.py 671 lines Fingerprint store src/neural_chat/titan_risk_agent.py 287 lines Risk validation data/regime_labels.json 44 regimes Labeled behaviors
28-slide deep dive into architecture, market, team, and financials.