Institutional-Grade Quantitative Backtesting & Strategy Validation Engine
High-throughput Python backtesting suite capable of vectorized exploratory sweeps across millions of historical candles, tick-level order fills with dynamic slippage modeling, and Monte Carlo confidence intervals.
The Problem
Retail backtesting tools suffer from fatal lookahead bias, unmodeled spread spikes during session closes, and absence of realistic slippage. Strategies that show 80% win rates in naive tests frequently collapse in live markets.
The Solution
Engineered an institutional Python suite that splits strategy exploration into ultra-fast vectorized sweeps and validates candidate parameters using an event-driven execution simulator with variable latency, floating spreads, and commission drag.
Vectorized Analytics Engine
# Vectorized Quantitative Risk & Performance Analytics
import numpy as np
import pandas as pd
def evaluate_trading_strategy(returns_series: pd.Series, risk_free_rate: float = 0.02) -> dict:
cumulative = (1 + returns_series).cumprod()
peak = cumulative.cummax()
drawdown = (cumulative - peak) / peak
max_dd = drawdown.min()
annualized_return = returns_series.mean() * 252
annualized_vol = returns_series.std() * np.sqrt(252)
sharpe = (annualized_return - risk_free_rate) / (annualized_vol + 1e-9)
downside_vol = returns_series[returns_series < 0].std() * np.sqrt(252)
sortino = (annualized_return - risk_free_rate) / (downside_vol + 1e-9)
calmar = annualized_return / abs(max_dd) if abs(max_dd) > 0 else 0
return {
"Total Return": f"{round((cumulative.iloc[-1] - 1) * 100, 2)}%",
"Max Drawdown": f"{round(max_dd * 100, 2)}%",
"Sharpe Ratio": round(sharpe, 2),
"Sortino Ratio": round(sortino, 2),
"Calmar Ratio": round(calmar, 2)
}"Majid's backtesting architecture prevented us from deploying overfitted strategies. His slippage modeling and Monte Carlo simulations gave us the statistical confidence to scale capital across global sessions."
