Strategies
Quantitative strategies presented with equity and risk metrics, rule validation, and full trade records.
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Explore Strategies & Portfolios
Select rules based systems or diversified bundles across futures, equities, crypto, and FX, all with transparent backtests, each presented with verifiable metrics and trade records.
Portfolios
Correlation aware bundles targeting steadier performance across assets and market conditions.
Explore portfolios
Quantitative Analysis Workflows
Institutional grade due diligence is embedded. Monte Carlo resampling and walk-forward tests verify edge and mitigate overfitting.
Audit grade backtests
Realistic P&L with trading frictions and embedded bias controls.
Walk forward validation
Walk forward analysis to confirm durable performance.
Monte Carlo simulations
Randomized paths and trade order to pressure-test the edge.
Trade transparency
Full trade history: entries, exits, drawdowns, and timestamps.
Portfolio construction
Multi asset diversification with correlation controls.
Integrated Risk Controls
Position sizing and risk limits integrated across all strategies.
Market Challenge
Markets reward systematic, data driven edge over intuition. Most backtests suffer from look ahead bias, curve fitting, and regime blindness, creating fragile strategies that collapse in live trading. Investors need a disciplined quantitative framework that isolates true signal, withstands market turbulence, and adapts as conditions evolve.
Algorithmic Strategy Research
We start with an economic hypothesis that explains why an edge may exist. We translate that idea into simple, testable rules and apply realistic data treatment and costs. Candidates that meet quality thresholds move to validation.
Define the hypothesis
Document the edge rationale such as trend persistence, mean reversion, breakouts, seasonality, carry, or market structure.
Specify the rules
Write deterministic entries and exits, timing and holding period, order model, and risk rules.
Prepare data and markets
Apply contract rolls and corporate actions, set session hours, filter for liquidity, and include financing, funding, and fees.
Set parameter ranges
Use sensible ranges and guardrails, favoring stability and simplicity over curve fit.
Test across assets
Evaluate in futures, equities, crypto, and FX, across multiple time frames and market regimes.
Promote candidates
Screen on net results, risk and stagnation, trade count, and rule clarity. Only qualified systems advance to the validation queue.
Robustness Validation Engine
Each strategy candidate is examined for stability, realism, and transparency. We use walk forward studies to track performance through time, Monte Carlo resampling to size dispersion and tail risk, sensitivity maps to confirm robust parameter regions, regime checks to test behavior in different market states, strict data and bias controls, and execution assumptions that match live trading.
Walk forward testing
Rules selected on one window are evaluated on the next. Rolling windows track stability and flag performance decay.
Monte Carlo analysis
Resamples price paths and trade order to estimate the range of outcomes and tail losses.
Parameter stability
Small changes in parameters should not change results materially. We map sensitivity to confirm robust zones.
Market state checks
Verifies behavior in trending and ranging markets and across different activity levels.
Integrity controls
No look ahead or survivorship effects. Corporate actions and contract rolls handled correctly. Results include fees, slippage, and financing.
Execution realism
Order models respect liquidity and latency. Size limits, stop logic, and cost assumptions.
Investment Perfomance Audit
Independent review that reconstructs net results, separates skill from luck, and turns findings into actions.
Equity
Investment
Benchmarks
Financial
01
Ingest trade logs and statements; construct net equity including fees, slippage, and financing.
02
Measure peak to trough loss, recovery time, stagnation days, and rolling returns.
03
Call out regime behavior and risk concentrations; note sizing and safeguard adjustments.
Responsible Capital Allocation
A disciplined approach built on clear reporting, consistent rules, and measured guidance.
Transparency
Equity and risk metrics reported net of fees, slippage, and financing, with trade records.
Reliable execution
Deterministic entries and exits that account for real costs, slippage, and latency.
Portfolio guidance
Diversify across assets with correlation review and overlap control.
FAQ
Frequently asked questions
What is RatioStreet?
RatioStreet is a quantitative trading platform that helps traders and investors discover, analyze, and use professionally built algorithmic trading strategies across multiple markets, including futures, forex, crypto, and equities.
Instead of relying on emotions or manual decision-making, RatioStreet focuses on data-driven, rules-based strategies that are:
- Fully backtested with realistic trading conditions (fees, slippage, drawdowns).
- Statistically validated using robustness testing such as Monte Carlo simulations and walk-forward analysis.
- Designed to perform across different market conditions, not just in ideal scenarios.
Our goal is simple:
To make institutional-grade quantitative strategies accessible to individual traders without requiring coding skills.
Users can explore performance metrics, risk profiles, and historical trade behavior before deciding to trade or integrate a strategy into their own portfolio.
How can I find out that strategies are going to be profitable also in real trading?
No strategy can guarantee future profits. At RatioStreet, every strategy is tested using realistic market conditions, including fees and slippage, and validated with advanced robustness tests such as walk-forward analysis and Monte Carlo simulations.
You can fully review each strategy’s performance metrics, drawdowns, and trade history before using it. For better risk control, strategies can also be combined into diversified portfolios.
What is Monte Carlo simulation in trading?
Monte Carlo simulation is a powerful statistical technique I use to analyze the probability characteristics of trading strategies. The core idea is that historical trades will occur in the future, just in some different and unknown order. By scrambling your historical trades into thousands of different sequences, you generate thousands of possible equity curves and can calculate probabilities for key metrics like maximum drawdown, annual returns, consecutive losses, and return-to-drawdown ratios. For example, if your backtest showed 3 consecutive losses but Monte Carlo reveals a 40% chance of 4 consecutive losses, you won't panic and abandon your strategy after that fourth loss in live trading.
How many strategies should I trade at once?
The ideal number depends on your capital and risk management approach, but I strongly recommend trading multiple non-correlated strategies for proper diversification. Trading 5 bitcoin strategies is pointless if they're highly correlated - the idea behind multiple strategies is to reduce risk through diversification, not concentrate or magnify it.
Before adding any new strategy to your portfolio, you should examine and compare it to your existing strategies to ensure low correlation.