Systematic
Strategy Research
Built on Evidence.
AlgoQuantFoundry is a rigorous algorithmic trading research platform. We develop, backtest, and validate systematic strategies with disciplined risk controls — before any consideration of live markets.
Platform
A Complete Research Infrastructure
Every component of the platform is designed around the principle that evidence comes before execution.
Strategy Research
Develop and study systematic trading strategies using rigorous hypothesis-driven research. Currently focused on Supertrend-based approaches for Silver Futures.
Backtesting Engine
Evaluate strategy performance on historical data with detailed analytics: equity curves, drawdown analysis, trade logs, and performance attribution.
Risk Management
Built-in risk controls including position sizing, stop-loss management, maximum drawdown limits, and portfolio-level exposure constraints.
Analytics Dashboard
Centralised dashboard presenting all active research strategies with live performance metrics, equity curves, and recent trade summaries.
Paper Trading (Planned)
Future capability: forward-test research strategies in a simulated environment with real market data feeds before any live deployment consideration.
Parameter Optimisation
Systematic parameter sweep and walk-forward analysis to identify robust parameter sets and avoid overfitting to historical in-sample data.
Current Research
Silver Futures Strategy Suite
Three distinct Supertrend-based approaches — each with different risk-reward characteristics.
Base Supertrend System
Silver Futures (SI)
Total Return
+45.2%
Sharpe Ratio
0.85
Max Drawdown
-18.4%
Win Rate
42.5%
Multiple Confirmation System
Silver Futures (SI)
Total Return
+62.8%
Sharpe Ratio
1.25
Max Drawdown
-12.1%
Win Rate
48.2%
Target/Stop-loss Integration
Silver Futures (SI)
Total Return
+55.4%
Sharpe Ratio
1.65
Max Drawdown
-8.5%
Win Rate
52.4%
Methodology
Evidence-Based Research Workflow
Every strategy goes through a disciplined, repeatable research process before results are considered meaningful.
Hypothesis Formation
Define a clear, testable market hypothesis grounded in market microstructure or price-action research.
Data Collection & Cleaning
Source high-quality historical futures tick and OHLCV data. Validate for gaps, splits, and survivorship bias.
Strategy Development
Translate the hypothesis into explicit entry, exit, and sizing rules. Parameterise only where justified.
In-Sample Backtesting
Test on a fixed historical window with full performance attribution — return, drawdown, trade statistics.
Walk-Forward Validation
Validate on out-of-sample periods to detect overfitting. Sensitivity analysis across parameter ranges.
Risk & Deployment Review
Apply position sizing, risk limits, and stress tests before any consideration of paper or live trading.
Risk Management
Risk Controls Are Not Optional —
They Are the Foundation.
Every strategy in AlgoQuantFoundry is built with risk management as a first-class concern, not an afterthought. We do not pursue returns at the expense of sound position management.
Risk Framework →Capital Preservation First
No strategy is deployed without explicit stop-loss and maximum drawdown thresholds. Protecting the research base is the primary objective.
Defined Risk Per Trade
Every trade has a predetermined maximum risk expressed as a fixed percentage of account equity, not arbitrary dollar amounts.
Drawdown Circuit Breakers
Automated halts trigger when cumulative drawdown exceeds predefined thresholds, preventing cascading losses during adverse market regimes.
No Leverage Without Analysis
Leverage decisions are made based on Kelly-derived position sizing and volatility-adjusted models, not intuition or greed.
Equity Curve — 12-Month Backtest
All Strategies · Silver Futures
Backtesting
Rigorous Historical Analysis Before Any Forward Test
Every research strategy is subjected to comprehensive backtesting across multiple market regimes, volatility environments, and time periods. We measure what matters: risk-adjusted returns, not just raw performance.
Full equity curve analysis with drawdown overlay
Trade-level statistics: duration, slippage, MAE/MFE
Profit factor, Sharpe ratio, and Sortino ratio
Walk-forward out-of-sample validation
Monte Carlo simulation for robustness testing
Next Phase
Paper Trading — Forward Testing in a Safe Environment
After a strategy passes rigorous backtesting and risk review, the next step is forward testing with real market data in a fully simulated paper trading environment. No real capital is ever involved at this stage.
Phase 1 · Current
Research & Backtesting
Phase 2 · Planned
Paper Trading
Phase 3 · Future
Live Evaluation
Join the Waitlist for Early Access
AlgoQuantFoundry is in active development. Register your interest to receive updates, early access to research outputs, and platform invitations.
Research platform only. No trading services offered. All performance data is simulated backtesting.