🧪 QuantBacktest — Institutional Multi-Asset Backtesting & Research Suite

An institutional-grade, multi-asset quantitative backtesting and portfolio research framework featuring Walk-Forward Optimization (WFO), Monte Carlo Risk Resampling (1,000+ bootstraps), realistic broker microstructure frictions (slippage, commissions, margin drag), and comprehensive tewarsheet risk analytics (Sharpe, Sortino, Calmar, Value-at-Risk VaR, and Maximum Drawdown).

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💼 Need Custom Quantitative Systems, Trading Strategies, or Backtesters?

I develop production-grade algorithmic backtesting platforms, high-frequency execution engines, and risk models for quant funds, prop desks, and private syndicates.

• 💬 Telegram: @JanzebBlockdev
• 💼 Fiverr Profile: Verified Quantitative Developer (Fiverr)

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🏗️ System Architecture

flowchart TD
    subgraph Data_Engine["📡 Market Data Layer"]
        Data["Multi-Asset Ingestion\n(Equities • Index Futures • Crypto • FX)"]
        Clean["Bar Normalization & Time-Series Alignment"]
        Data --> Clean
    end

    subgraph Strategy_Engine["🧠 Quantitative Strategies"]
        Trend["EMA Ribbon + Donchian Breakout"]
        MeanRev["Bollinger Bands + RSI Mean Reversion"]
        Breakout["Opening Range Breakout (ORB) + Keltner"]
    end

    subgraph Simulation_Core["⚙️ Execution & Portfolio Simulator"]
        Micro["Broker Microstructure Model\n(Basis-Points Slippage + IBKR Tiered Commissions)"]
        Blotter["Event Accounting & Trade Blotter\n(Cash • Margin Cushion • PnL Attribution)"]
    end

    subgraph Validation_Suite["🔬 Institutional Validation & Robustness"]
        WFO["Walk-Forward Optimization (WFO)\n(Sliding In-Sample Train vs Out-of-Sample Test)"]
        MC["Monte Carlo Risk Resampling\n(1,000 Bootstraps • Ruin Probability • Tail VaR)"]
        Tearsheet["Tearsheet Analytics\n(Sharpe • Sortino • Calmar • MDD • Expectancy)"]
    end

    Clean --> Trend & MeanRev & Breakout
    Trend & MeanRev & Breakout --> Micro
    Micro --> Blotter
    Blotter --> WFO
    Blotter --> MC
    Blotter --> Tearsheet

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⚡ Key Capabilities

1. 🔬 Walk-Forward Optimization (WFO) & Efficiency (WFE)

   • Avoids the deadly trap of curve-fitting by dividing backtest history into sliding In-Sample (Train) and Out-of-Sample (Test) windows. Calculates the Walk-Forward Efficiency (WFE %) ratio to mathematically prove strategy durability.

2. 🎲 Monte Carlo Risk & Ruin Simulation (1,000+ Bootstraps)

   • Resamples real trade sequence distributions across 1,000 to 10,000 iterations to determine 5th-percentile worst-case equity, median drawdown, and the statistical Probability of Ruin (chances of encountering a 20%, 30%, or 50% drawdown).

3. 🏛️ Realistic Microstructure & Broker Frictions

   • Incorporates real-world execution drag:
     • Slippage Model: Configurable basis-points (bps) slippage reflecting market depth.
     • Commissions: Interactive Brokers tiered equity ($0.005/share) and futures ($0.85/contract) fee structures.
     • Cash & Margin Accounting: True portfolio balance, position sizing, and leverage constraints.

4. 📊 Comprehensive Hedge-Fund Tearsheets

   • Computes risk-adjusted performance metrics:
     • Risk-Adjusted Return: Annualized Sharpe Ratio ($R_f$ adjusted), Sortino Ratio (downside volatility only), and Calmar Ratio.
     • Tail Risk: Value-at-Risk (Daily VaR 95%) and Conditional VaR (CVaR / Expected Shortfall).
     • Trade Efficiency: Win Rate %, Profit Factor, Payoff Ratio, Average Win/Loss, and Mathematical Expectancy.

5. 🏆 Cross-Strategy Portfolio Leaderboard

   • Benchmark multiple quantitative models side-by-side on the same asset class and historical regime.

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🖥️ Terminal Quantitative Tearsheet Preview

================================================================================
                    QUANTITATIVE RESEARCH TEARSHEET
Strategy: EMA_Donchian_Trend             Benchmark: SPY
================================================================================
PORTFOLIO CAPITAL & RETURNS                RISK & VOLATILITY ANALYTICS
---------------------------------------    -------------------------------------
Initial Capital:        $100,000.00        Annualized Volatility:      14.20%
Final Equity:           $134,820.50        Maximum Drawdown (MDD):      7.10%
Total Net Return:             34.82%       Daily VaR (95% Conf):        1.25%
CAGR (Annualized):            15.40%       Daily CVaR / Shortfall:      1.82%
Sharpe Ratio (Rf=4.5%):        1.48        Calmar Ratio:                2.17
Sortino Ratio:                 2.15

TRADE EXECUTION & MICROSTRUCTURE
--------------------------------------------------------------------------------
Total Completed Trades:  28          Win Rate (%):           64.29%
Profit Factor:           2.24        Payoff Ratio (Win/Loss): 1.82
Trade Expectancy:       $1,243.59    Avg Duration:           14.2 bars
================================================================================

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🚀 Quickstart

1. Installation

git clone https://github.com/jahanzaib-codes/QuantBacktest.git
cd QuantBacktest
python -m venv .venv

# Windows
.venv\Scripts\activate
# Linux / macOS
source .venv/bin/activate

pip install -r requirements.txt

2. Environment Configuration

Copy example settings:

cp .env.example .env

Edit .env (optional defaults are already pre-configured):

INITIAL_CAPITAL=100000.0
ACCOUNT_CURRENCY=USD
DEFAULT_COMMISSION_PER_SHARE=0.005
SLIPPAGE_BPS=3.0
DEFAULT_MONTE_CARLO_RUNS=1000

3. Run Strategy Backtest & Robustness Suite

# Run Trend-Following Strategy with Monte Carlo & Walk-Forward Optimization
python cli.py --strategy trend --symbol SPY

# Run Mean Reversion Strategy
python cli.py --strategy mean_reversion --symbol AAPL

# Compare Multiple Strategies on Leaderboard
python cli.py --strategy compare --symbol SPY

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🧰 Strategy Implementation Protocol

Custom strategies inherit from BaseStrategy and implement generate_signals:

from src.strategies.base import BaseStrategy
import pandas as pd
import numpy as np

class CustomBreakout(BaseStrategy):
    def __init__(self, period: int = 20):
        super().__init__("Custom_Breakout", {"period": period})
        self.period = period

    def generate_signals(self, df: pd.DataFrame) -> pd.DataFrame:
        data = df.copy()
        high_roll = data["high"].rolling(self.period).max().shift(1)
        data["signal"] = np.where(data["close"] > high_roll, 1, 0)
        data["position_size"] = np.where(data["signal"] == 1, 0.95, 0.0)
        return data

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📄 License

Distributed under the MIT License. See LICENSE for details.

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🌟 Maintained by Jahanzaib

• GitHub: @jahanzaib-codes
• Fiverr: Hire for Quant & Trading Bot Development
• Telegram: @JanzebBlockdev