MokshaGPT
Stock Market Insights by AI
Validate, Optimize & Ensemble
Trading Strategies with AI
MokshaGPT brings institutional-grade quantitative backtesting tools to retail traders. No coding. No complex indicators scripting. Formulate your ideas in plain English and let our AI do the work.
🔮Test Your Strategy Idea
Type what you want to achieve. Our AI will analyze your query and route you to the correct tool prefilled!
Three Pillars of AI Quantitative Refinement
MokshaGPT segments backtesting into three tailored execution layers. Find the exact fit for your strategy criteria.
AI Strategy Backtester
Pillar 1: Plain-English ValidationDescribe strategy rules in natural English (e.g., indicators, stock tickers, timeline, transaction fees). Our backend translates your input, runs a vectorized mathematical simulation on historical market data, and delivers instant, readable reports.
✓ Full trades logs & drawdown details
✓ Volatility, Sharpe, expectancies
✓ Vectorbt-backed high-speed engine
Backtest Optimizer
Pillar 2: Autonomous LangGraph LoopsSet target thresholds (e.g. Sharpe ratio > 1.5, Win Rate > 60%). An autonomous LangGraph loop will execute backtests, analyze the performance, adjust parameter rules, and run again—until your strategy objectives are fully met.
✓ Continuous agentic execution loop
✓ Automatic stop-loss and trailing tuning
✓ Detailed parameter logs per iteration
Ensemble Builder
Pillar 3: Uncorrelated DiversificationAvoid single-strategy risk. Enter a single quant hypothesis, and the AI generates 3 distinct, uncorrelated models (Trend Following, Mean Reversion, Breakout). They run concurrently, aggregating their equity vectors to cushion max drawdowns.
✓ 3 diverse quantitative strategies
✓ Mathematical daily equity summation
✓ Dramatically reduced portfolio drawdown
Why Upgrade to AI-Powered Backtesting?
Zero Coding Barrier
Say goodbye to hours spent debugging complex PineScript, Python code, or MT5 strategy scripts. Formulate your logic naturally.
No Overfitting Biases
Optimizing rules manually leads to curve-fitting. The Backtest Optimizer analyzes failures scientifically, preserving real-world edge.
Institutional Metrics
Evaluate strategies like a hedge fund. Access deep metrics: Sharpe ratios, Sortino ratios, Calmar drawdown multipliers, and expectations.
Global Data Sources
Supports NYSE, NASDAQ, NSE (India), FTSE (London), DAX (Germany), Crypto pairs, Forex, and physical Commodity futures.
Understanding Quantitative Validation Metrics
Our backtester computes institutional metrics. Here is a guide to what they mean and how they measure strategy performance:
📊 Sharpe Ratio
Sharpe Ratio measures the risk-adjusted return of your strategy. It divides the excess returns of the portfolio above the risk-free rate by the daily standard deviation of those returns. A Sharpe ratio above 1.0 is considered decent, above 2.0 is excellent, and above 3.0 represents a world-class trading system.
📉 Maximum Drawdown (Max DD)
Maximum drawdown measures the largest peak-to-trough decline in the value of the portfolio before a new peak is achieved. It is a critical gauge of historical downside risk. Most professional quants target keeping drawdown below 15% to minimize capital impairment and behavioral stress.
⚖️ Profit Factor
Profit Factor is calculated as gross profit divided by gross loss for all executed trades during the backtesting duration. A Profit Factor of 1.0 means you broke even. Target a profit factor of 1.5 to 2.5 for a robust strategy that can absorb shifting market regimes.
💎 Sortino Ratio
Similar to the Sharpe Ratio, but the Sortino Ratio only penalizes negative volatility (downside risk). It isolates the variance of negative returns, giving a cleaner view of whether a strategy’s risk comes from actual losses or rapid positive surges.
Frequently Asked Questions (FAQ)
How do I backtest a stock trading strategy without coding?
With MokshaGPT, you don't need any programming skills. You simply type your strategy parameters in natural English (e.g. "Buy AAPL when 20 EMA crosses above 50 EMA, hold for 1 year"). Our advanced AI compiler parses your rules, structures them into vectorized Python commands, and runs a comprehensive historical simulation on our institutional database.
What is a LangGraph autonomous strategy optimizer?
A LangGraph autonomous optimizer represents a state-of-the-art agentic loop. Instead of running a single backtest and guessing what parameters to tweak next, our agent iteratively executes simulations, reads Sharpe/drawdown metrics, analyzes the failures, adjusts thresholds autonomously, and repeats the loop until the strategy meets your desired performance benchmarks.
How does the Ensemble Builder reduce maximum drawdown?
The Ensemble Builder automatically constructs three distinct, uncorrelated strategies—typically combining a trend follower, a mean reversion model, and a volatility breakout engine. Because these strategies rely on different mathematical signals, their losing periods rarely overlap. When one model experiences a dip, the others cushion the total equity value, yielding a significantly smoother overall portfolio curve.
Is backtesting historical data 100% accurate?
Backtests simulate historical performance. While highly accurate for verifying if a strategy had an edge in the past, real-world execution contains slippage, transaction fees, liquidity constraints, and latency. MokshaGPT factors in standard transaction fees (approximated at 0.1% per trade) to ensure our simulations reflect realistic trading costs.