BacktestBase Documentation

Complete guide to using BacktestBase for systematic trading strategy analysis and portfolio optimization

Table of Contents

What is BacktestBase?

BacktestBase is the comprehensive platform for systematic traders who want to transform their individual TradingView strategies into a professional trading foundation. Unlike simple analysis tools, BacktestBase serves as your central hub for strategy organization, portfolio creation, and risk management.

Key Benefits:

  • Strategy Organization: Automatically organize all your TradingView backtests in one searchable database
  • Portfolio Creation: Combine multiple strategies into diversified portfolios with scientific weighting
  • Risk Analysis: Run Monte Carlo simulations using your actual strategy performance data
  • Performance Tracking: Compare strategies side-by-side with comprehensive metrics
  • Professional Foundation: Build a systematic approach to trading with data-driven insights

Account Setup

Getting Started

  1. Sign Up: Create your account using Google OAuth - secure and instant
  2. Dashboard Access: Once signed in, you'll be redirected to your personal dashboard
  3. First Upload: Start by uploading your first TradingView strategy export

💡 Pro Tip

Before uploading, organize your TradingView strategy exports in folders by symbol, timeframe, or strategy type. This will help you build a more organized strategy database.

Dashboard Overview

Your dashboard is the command center for all your trading strategies and analysis.

Dashboard Sections:

📊 Quick Stats

  • • Total strategies uploaded
  • • Combined net profit
  • • Average win rate
  • • Best profit factor

🔧 Strategy Management

  • • Sort by any performance metric
  • • Edit strategy names and descriptions
  • • Delete outdated strategies
  • • View detailed analysis

Uploading TradingView Strategies

Step-by-Step Upload Process

1. Export from TradingView

  1. Run your strategy in TradingView Strategy Tester
  2. Click the "Export" button in the Strategy Tester tab
  3. Choose "Excel" format (.xlsx)
  4. Save the file to your computer

2. Upload to BacktestBase

  1. Go to the Upload page in BacktestBase
  2. Drag and drop your .xlsx file or click to browse
  3. Wait for automatic parsing (usually under 30 seconds)
  4. Review the extracted data preview
  5. Add a descriptive name and optional description
  6. Click "Save Strategy" to add it to your database

3. What Gets Extracted

Performance Metrics:
  • Net Profit ($ and %)
  • Gross Profit/Loss
  • Profit Factor
  • Sharpe & Sortino Ratios
  • Maximum Drawdown
Trade Data:
  • Complete trade history
  • Win/Loss statistics
  • Average winning/losing trades
  • Largest winning/losing trades
  • Long/Short performance breakdown

⚠️ Important Notes

  • • Only TradingView .xlsx exports are currently supported
  • • Files must be under 10MB in size
  • • Free accounts limited to 3 strategies
  • • All data is encrypted and secure

Strategy Analysis

BacktestBase provides comprehensive analysis of your trading strategies with professional-grade metrics and insights that go beyond basic TradingView statistics.

Available Analysis:

📈 Performance Metrics

  • • Profit Factor & Risk-Adjusted Returns
  • • Sharpe, Sortino, and Calmar Ratios
  • • Maximum & Average Drawdown Analysis
  • • Win Rate & Risk/Reward Ratios
  • • Long vs Short Performance Breakdown

🔍 Trade Analysis

  • • Complete trade history with P&L
  • • Average P&L per trade
  • • Average winning/losing trade sizes
  • • Largest winning/losing trades
  • • Long vs Short directional performance

Strategy Comparison

Use the dashboard sorting and filtering features to compare strategies across different:

  • Market symbols (EURUSD vs GBPUSD vs crypto)
  • Timeframes (15m vs 1h vs 4h strategies)
  • Strategy types (trend-following vs mean-reversion)
  • Risk profiles (high Sharpe vs low drawdown)

Portfolio Creation

Transform individual strategies into diversified portfolios using professional weighting algorithms and concrete benefit analysis.

Portfolio Weighting Methods

🎯 Account Size Weighting

Weight strategies based on their original backtest capital allocation. Best for realistic portfolio representation when strategies were tested with different account sizes.

⚖️ Equal Weight Distribution

Simple 1/N allocation across all selected strategies. Provides maximum diversification and is ideal when you want equal exposure to each trading approach.

🛡️ Risk-Adjusted Weighting

Allocate more capital to strategies with lower maximum drawdown. Optimizes for risk-adjusted returns and smoother portfolio performance.

Portfolio Benefits Analysis

📊 Capital Efficiency

Compare total portfolio capital requirements vs individual strategy needs.

🛡️ Risk Reduction

Measure portfolio drawdown improvement vs worst individual strategy.

📈 Trading Consistency

Analyze increased trading opportunities through combined signals.

🌍 Market Coverage

Evaluate diversification across symbols and timeframes.

Monte Carlo Stress Testing

Research-Backed Methodology: BacktestBase uses institutional-grade Monte Carlo stress testing for individual strategy analysis, providing superior accuracy through pure mathematical trade randomization and skipping methodologies based on established statistical principles.

Our Monte Carlo system evaluates individual strategy robustness using actual historical trades - no forward projections or statistical assumptions, just rigorous testing of real performance data under various execution scenarios. Monte Carlo stress testing is currently available for single strategies only.

🔬 Scientific Foundation

Historical Bootstrap Method

Based on Efron & Tibshirani (1993) bootstrap theory

  • • Uses actual trade sequences, not synthetic distributions
  • • Eliminates parametric assumptions
  • • Superior to normal distribution models

Block Bootstrap Technique

Advanced implementation of Politis & Romano (1994)

  • • Preserves temporal correlation structures
  • • Captures regime-dependent relationships
  • • More realistic than IID sampling

🎯 Portfolio Correlation Innovation

The Challenge: Traditional approaches require complex correlation matrices between strategies. This fails when dealing with unknown instruments or non-stationary correlations.

Our Solution: Chronological trade ordering naturally captures correlations. If strategies are correlated, their winning/losing trades cluster during the same time periods - no modeling required, just historical reality.

⚙️ Technical Implementation

Enhanced Strategy Identity Preservation:

  1. Trade Extraction: Parse all historical trades from TradingView exports maintaining chronological order
  2. Random Skipping: Each simulation randomly skips 0% to user-defined percentage of trades (preserves temporal gaps)
  3. Limited Sequence Randomization: Only randomize user-defined percentage of trades (default 10%) to preserve strategy characteristics
  4. Chronological Foundation: Maintain chronological order for majority of trades to preserve correlations and regime dependencies
  5. Peak-to-Trough Analysis: Calculate drawdowns using industry-standard peak capital methodology on final sequences
  6. Robustness Scoring: 30-point system based on drawdown control and profit consistency across all simulations

Portfolio Stress Testing Innovation:

  1. Chronological Combination: All trades from selected strategies combined by actual entry date/time
  2. Strategy Identity Preservation: Enhanced methodology maintains each strategy's temporal characteristics
  3. Weight Application: Portfolio allocation weights applied to individual trade P&L values
  4. Controlled Stress Testing: Limited randomization prevents complete strategy destruction while adding realistic variance
  5. Correlation Preservation: Natural clustering of correlated trades maintains market relationships through chronological ordering
  6. Institutional Metrics: Same peak-to-trough calculations and robustness scoring as single strategies

🏆 Advantages Over Traditional Methods

✅ Superior Accuracy

  • • No correlation estimation errors
  • • Captures regime-dependent relationships
  • • Uses actual market correlation history
  • • Handles unknown/exotic instruments

⚙️ Technical Superiority

  • • Block bootstrap without complexity
  • • Eliminates IID assumptions
  • • Preserves temporal structures
  • • Scalable to any portfolio size

📊 Key Parameters & Interpretation

Simulation Count

500-10,000 independent runs

Higher counts increase statistical significance

Trade Skip Percentage

0% to user-defined maximum per simulation

Simulates realistic execution gaps

Sequence Randomization %

Default 10% of trades randomized

Preserves strategy identity while adding stress

30-Point Robustness Scoring System

Absolute Drawdown Control (15 points)
  • • 5th, 50th, 95th percentile analysis
  • • 5 points: <20% drawdown
  • • 3 points: 20-30% drawdown
  • • 1 point: 30-50% drawdown
  • • 0 points: >50% drawdown
Profit Consistency (15 points)
  • • Net Profit ÷ Max Drawdown ratios
  • • 5 points: Ratio > 4.5
  • • 3 points: Ratio 3.0-4.5
  • • 1 point: Ratio 2.0-3.0
  • • 0 points: Ratio < 2.0

Grades: A+ (28-30), A (25-27), B+ (22-24), B (19-21), C+ (16-18), C (13-15), D (10-12), F (0-9)

🎯 Practical Applications

Strategy Validation

  • • Test robustness under execution stress
  • • Identify strategies sensitive to timing
  • • Plan for realistic trading conditions

Portfolio Optimization

  • • Validate multi-strategy combinations
  • • Test correlation assumptions
  • • Optimize position sizing

Risk Management

  • • Understand worst-case scenarios
  • • Plan capital requirements
  • • Set realistic expectations

Institutional Analysis

  • • Professional-grade stress testing
  • • Research-backed methodology
  • • Academic foundation compliance

📚 Academic References

  • • Efron, B., & Tibshirani, R. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC
  • • Politis, D. N., & Romano, J. P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association
  • • Bailey, D. H., & López de Prado, M. (2014). The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality
  • • Getmansky, M., Lo, A. W., & Makarov, I. (2004). An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns
  • • Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press

Frequently Asked Questions

What file formats do you support?

Currently, BacktestBase supports TradingView Strategy Tester XLSX exports with full 5-sheet parsing. Support for additional platforms and formats is planned for future releases.

How secure is my trading data?

We use enterprise-grade security with Google OAuth authentication, database encryption, and Row Level Security. Your data is isolated to your account and never shared. We are not a broker - just a secure analysis platform.

Can I analyze strategies from different markets?

Absolutely. BacktestBase works with any TradingView strategy regardless of market (Forex, crypto, stocks, futures) or timeframe. Build comprehensive portfolios across multiple markets and trading approaches.

What's the difference between BacktestBase and TradingView?

TradingView is excellent for backtesting individual strategies. BacktestBase takes those results and transforms them into a comprehensive trading foundation - organizing strategies, creating portfolios, running risk simulations, and building systematic approaches to trading.

Can I delete my account and data?

Yes, you have full control over your data. You can delete individual strategies or your entire account at any time. Data deletion is permanent and immediate.

Need More Help?

Can't find what you're looking for? Our support team is here to help with any questions about BacktestBase features, strategy analysis, or portfolio optimization.

Contact SupportEmail Directly

Response Time: We typically respond within 24 hours. Include your account email and describe your question in detail for the fastest response.

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