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Quantitative Architecture
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Systematic Strategies: Architecture, Signal Engine, and Portfolio Allocation

A foundational overview of the vectorized quantitative signal generation framework, unified contracts, and multi-strategy allocation models.

Target Audience: Quantitative Analysts, Algorithmic Traders & Portfolio Managers

Key Takeaways for Investors & Traders

Unified Base Architecture

All strategy classes inherit from BaseSignalGenerator, enforcing strict schema validation and vectorized execution grouped by symbol.

Dynamic Strategy Registry

Strategies register dynamically via decorator and are instantiated via a factory lookup supporting custom lookback and threshold hyperparameters.

Standardized Output Contract

Every strategy emits a consistent Polars DataFrame featuring timestamp, symbol, raw_score, signal (-1.0, 0.0, +1.0), and directional tags.

Multi-Strategy Blending

Combines uncorrelated momentum, mean-reversion, and breakout models to generate all-weather portfolio diversification.

Executive Summary

The Technosight Signal Generation Framework provides a high-throughput, vectorized signal generation library designed for multi-asset equity universes. Implemented with SIMD-accelerated columnar operations over unified End-of-Day (EOD) and intraday market data, all strategies adhere to standardized input validation, dynamic registry lookups, unified output schemas, and multi-horizon portfolio construction rules.

1

Signal Engine Architecture & Operational Flow

The signal generation framework processes raw equity market data into discrete, risk-adjusted trade directives through a multi-stage vectorized pipeline:

graph TD
    A[Market Data Feeds: EOD & 15m Bars] --> B[Vectorized Signal Engine: Polars SIMD Operations]
    B --> C[Strategy Generator: BaseSignalGenerator Subclasses]
    C --> D[Standardized Signal DataFrame: Timestamp, Symbol, Raw Score, Signal, Direction]
    D --> E[Multi-Timeframe EMA Regime Filter: 15m 50/200/800 Alignment]
    E --> F[Promising Score & Fundamental Enrichment Engine]
    F --> G[Downstream Consumers: Backtesting, Daily Reports & Live Execution]

Core Architecture Components

  1. Data Ingestion & Normalization: Historical daily OHLCV bars and intraday 15-minute series are ingested and stored in columnar formats, indexed by UTC timestamp and asset ticker.
  2. Vectorized Computation: All moving averages, rolling standard deviations, return differentials, and channel bounds are calculated using SIMD-accelerated vectorized operations partitioned across equity symbols.
  3. Execution Decoupling: Signal generation is decoupled from execution mechanics, producing raw conviction scores and normalized trigger flags suitable for simulation or live execution.
2

Strategy Registry & Factory Lookup System

Strategies register dynamically with the system registry through an automated decorator pattern and are instantiated through a centralized factory function create_signal_strategy(name, **params):

Strategy Name / Family Implementation Class Registered Aliases Default Primary Parameters
Mean Reversion MeanReversionStrategy mean_reversion, mean_reversion_strategy lookback_period=20, entry_threshold=2.0, exit_threshold=0.5
Standard Momentum MomentumStrategy momentum, momentum_standard, momentum_strategy lookback_period=252, long_percentile=0.8, short_percentile=0.2, cross_sectional=False
Momentum Omega MomentumOmegaStrategy momentum_omega, momentum_omega_strategy long_term_window=252, short_term_window=21, volatility_window=126, threshold=0.0
Statistical Arbitrage StatisticalArbitrageStrategy statistical_arbitrage, stat_arb, statistical_arbitrage_strategy lookback_period=60, entry_threshold=2.0, exit_threshold=0.5, stop_loss_threshold=3.5
Trend Following TrendFollowingStrategy trend_following, sma_crossover, trend_following_strategy fast_period=50, slow_period=200
Volatility Breakout VolatilityBreakoutStrategy volatility_breakout, vol_breakout, volatility_breakout_strategy lookback_period=20, atr_multiplier=2.0

Each strategy validates its parameter bounds upon initialization to ensure mathematical sanity (e.g. fast_period < slow_period, entry_threshold > exit_threshold > 0.0).

3

Unified Output Schema Contract

Every strategy implements a generate_signals(eod_prices) method returning a standardized DataFrame with guaranteed schema columns, followed by model-specific technical indicators:

Column Name Data Type Value Domain Operational Description
timestamp Datetime[UTC] ISO 8601 Timestamps Historical bar calculation timestamp.
symbol Utf8 / String Ticker Strings (e.g. AAPL) Asset identifier.
raw_score Float64 $(-\infty, +\infty)$ Unbounded strategy factor conviction (positive = bullish, negative = bearish).
signal Float64 $\{-1.0, 0.0, +1.0\}$ Discrete execution trigger: +1.0 (Long), -1.0 (Short), 0.0 (Neutral/Flat).
direction Utf8 / String {'LONG', 'SHORT', 'NEUTRAL'} Human-readable string representation of signal.

Downstream engines—including portfolio optimizers, backtesting simulators, and candidate ranking algorithms—rely on this guaranteed schema, ensuring modular interoperability.

4

Comparative Strategy Matrix & Regime Suitability Guide

The six systematic strategies operate across complementary market regimes, timeframes, and mathematical principles:

Evaluation Dimension Mean Reversion Standard Momentum Momentum Omega Statistical Arbitrage Trend Following Volatility Breakout
Theoretical Base Market Microstructure, Overreaction Information Underreaction Risk-Adjusted Momentum Cointegration, Stationarity Serial Correlation, Price Drift Volatility Clustering
Typical Lookback 20 trading days 252 trading days 252d / 21d / 126d 60 trading days 50d / 200d 20 trading days
Input Price Columns close close close close close, high, low close, high, low, volume
Primary Score Metric Negative Z-Score ($-Z_t$) Cumulative Return ($R_{252}$) Vol-Adjusted Diff ($\Omega$) Negative Spread Z-Score Moving Average Spread % Normalized Band Distance
Risk Features Ornstein-Uhlenbeck Half-Life, RSI Cross-Sectional Deciles Volatility Scaling $|Z| \ge 3.5$ Structural Cutoff 2x ATR Chandelier Volatility Stop 1.5x / 3x ATR Bracket, Volume Squeeze
Optimal Regime Range-bound, low-to-moderate volatility Macro bull trend, high dispersion Sustained secular trends Stationary spreads, range-bound Strong secular trends, crisis runs Range-to-trend transition expansions
Adverse Regime Runaway directional trends Choppy sideways, V-reversals High erratic volatility Permanent structural breaks Choppy sideways consolidation False breakout whipsaws
Portfolio Role Range harvesting alpha Momentum trend alpha Core equity selection Market-neutral pair alpha Crisis alpha & tail hedge High-velocity momentum alpha
5

Multi-Strategy Portfolio Construction & Regime Allocation

To achieve steady capital growth across varying macroeconomic environments, institutional allocators combine multiple systematic strategies into an all-weather portfolio structure:

graph TD
    subgraph MultiStrategyPortfolio [Target Portfolio Allocation: 100%]
        direction TB
        subgraph TrendMomentum [Trend & Momentum: 50%]
            TF[Trend Following: 20%]
            MO[Momentum Omega: 20%]
            VB[Volatility Breakout: 10%]
        end
        subgraph MeanReversion [Mean Reversion & Stat Arb: 35%]
            SA[Statistical Arbitrage: 20%]
            MR[Mean Reversion: 15%]
        end
        subgraph LiquidityBuffer [Volatility & Liquidity: 15%]
            CASH[Volatility-Scaled Cash Reserve: 15%]
        end
    end

Allocation & Regime Switching Mechanics

  1. Uncorrelated Return Profiles: Trend Following and Statistical Arbitrage exhibit historically low or negative correlation. In choppy sideways markets, Statistical Arbitrage and Mean Reversion harvest short-term spreads while Trend Following maintains capital protection. In macro trending environments, Trend Following and Volatility Breakout capture extended multi-month trends while Statistical Arbitrage automatically exits diverging pairs via structural cutoffs.
  2. Dynamic Regime Gating via 15-Minute EMA Alignment: The platform's intraday EMARegimeFilter classifies assets into AAA (Bullish trend alignment), BBB (Bearish trend alignment), or NEUTRAL (Ranging) regimes. Bullish and bearish regimes are routed to Trend Following and Volatility Breakout, while neutral regimes are allocated to Mean Reversion and Statistical Arbitrage.
  3. Downstream Multi-Factor Scoring: Signals flow into candidate ranking engines that aggregate multi-scenario confidence scores, perform liquidity volume tranche checks, and cross-reference underlying company valuation metrics.