Architecture Roadmap

Current Status: Phase 3 (Hybrid Generative Intelligence)

Signal.Engine has successfully transitioned from a heuristic scanner to a sequence-aware reasoning agent. The current architecture leverages Supervised Fine-Tuning (SFT) for market "common sense" and Proximal Policy Optimization (PPO) for execution logic.

Phase 4: Portfolio Optimization & Risk Parity

The next immediate evolution focuses on moving from individual symbol predictions to a holistic portfolio view.

Global Risk Constraints: Implementation of a RiskManager module that monitors total exposure across the Nifty 500.
Dynamic Position Sizing: Shifting from fixed quantity trades (--qty 1) to Kelly Criterion or Volatility-Adjusted sizing.

Usage Example:

# Proposed execution with dynamic risk allocation
python -m src.trader_alpaca --universe NIFTY_NEXT_50 --risk_limit 0.02 --strategy risk_parity

Phase 5: Cognitive Sentiment Integration (LLM Reasoning)

Integrating unstructured data to provide macro-context to the LSTM-based "Brain."

News & Earnings Parsers: LLM agents (GPT-4/Claude) will summarize news sentiment and earnings calls into a "Sentiment Score."
Reasoning Injection: The HybridBrain will receive a sentiment vector as an additional feature input, allowing the model to stay "flat" during high-impact news events despite bullish technicals.

Public Interface:

# Planned configuration in .env
OPENAI_API_KEY="sk-..."
SENTIMENT_WEIGHT=0.3  # Balance between technicals and news

Phase 6: Multi-Asset & Cross-Market Correlation

Expansion beyond the Indian Equity market (Nifty) to global assets and correlated instruments.

Universal Adapters: Standardizing the data_loader.py to support Binance (Crypto), Interactive Brokers (Global Equities), and OANDA (Forex).
Correlation Mapping: The agent will recognize that a spike in Brent Crude or the USD/INR pair should trigger specific defensive routines in Auto and Paint stocks.

Configuration:

# config/universe.yaml
universes:
  - name: "Commodity_Linked"
    symbols: ["RELIANCE.NS", "BPCL.NS"]
    correlates: ["CL=F"] # Crude Oil Futures

Phase 7: Execution Sharding & High-Frequency Adaptation

Scaling the infrastructure to handle thousands of symbols with sub-second latency.

Distributed Processing: Moving from single-process scans to a task queue (Celery/Redis) where "Worker Agents" process different sectors in parallel.
Order Book Analysis: Integrating Level 2 data (Market Depth) into the Reinforcement Learning environment to reduce slippage in low-liquidity midcaps.
Infrastructure Change: Transitioning from a single Python script to a containerized microservice architecture.

Phase 8: Autonomous Swarm Intelligence

The final phase involves a multi-agent system where specialized "Sub-Agents" compete and collaborate.

Specialist Agents:
- The Bull: Trained specifically for parabolic trend following.
- The Bear: Trained for mean reversion and short-selling in high-volatility regimes.
- The Hedger: Dedicated to neutralising Delta during market uncertainty.
The Jury (Consensus Layer): A top-level Meta-Agent that allocates capital to the Sub-Agents based on the current "Market Regime" identified by the system.
Dashboard View: The React frontend will evolve to show the "Swarm Status," displaying which sub-agents are currently in control of the capital.

Roadmap Summary