What is a compound interest calculator?

A compound interest calculator helps you estimate the future value of your investments by calculating interest on both your initial principal and accumulated interest over time. Our calculator includes monthly contributions and shows you exactly how your money can grow.

How far back can I backtest stock data?

You can backtest investment strategies up to 50 years using real historical data from Yahoo Finance for major ETFs like SPY, QQQ, VTI, SCHD, and more. This allows you to see how your investment approach would have performed during different market conditions.

What is CAGR and why does it matter?

CAGR (Compound Annual Growth Rate) is the rate of return that would be required for an investment to grow from its beginning balance to its ending balance over a specific time period. It's important because it smooths out volatility and gives you a true sense of an investment's performance.

Which assets can I analyze with this tool?

You can analyze 18+ assets including: SPY (S&P 500), QQQ (Nasdaq), VTI (Total Stock Market), SCHD (Dividend Equity), VIG (Dividend Appreciation), international ETFs, real estate (VNQ), precious metals (GLD, SLV), bonds (BND), oil (USO), and cryptocurrencies (Bitcoin, Ethereum, Solana).

How does the portfolio builder work?

The portfolio builder allows you to create custom investment portfolios by allocating percentages to different assets. It automatically calculates the weighted average CAGR based on your allocations and historical performance data, helping you optimize your portfolio diversification.

Is this investment calculator free?

Yes! This investment calculator, backtesting tool, and portfolio analyzer are completely free to use. No registration required. We provide real-time historical data from Yahoo Finance at no cost.

Free Investment Calculator | Compound Interest, Backtesting & Portfolio Analysis Tool

Compound Interest Calculator

Discover the power of compound interest - Einstein's "8th wonder of the world" - and plan your financial future

Initial Investment

Monthly Contribution

Expected Annual Return (%)

Investment Period (Years)

Historical Stock Backtesting (Up to 50 Years)

See how your investment strategy would have performed with real historical market data from SPY, QQQ, VTI, and 17+ major ETFs and assets

Select Asset to Backtest:

Backtest Period

Fetching historical data from Yahoo Finance...

ETF Performance Analysis - CAGR Calculator

Analyze historical Compound Annual Growth Rate (CAGR) for 18+ ETFs including SPY, QQQ, VTI, SCHD, bonds, gold, and international markets

Time Period (Years)

Custom Portfolio Builder with Weighted CAGR

Build your custom investment portfolio and calculate the weighted average CAGR across multiple assets

CAGR Period

Portfolio Summary

Total Allocation

30-Year Weighted CAGR

Note: Total allocation should equal 100%. The CAGR will be calculated automatically as you add assets and allocations.

Machine Learning Algorithmic Trading

Explore practical machine learning approaches widely used in quantitative trading and algorithmic finance

Regression

Linear Regression

The foundational ML technique that models the relationship between input features (technical indicators, volume, etc.) and price movements using a linear equation. Despite its simplicity, it's often used as a baseline and can be surprisingly effective for trend prediction and feature importance analysis.

✓ Advantages: Extremely fast to train and deploy, highly interpretable coefficients show feature importance, works well as baseline, low computational requirements, no risk of overfitting with proper regularization (Ridge/Lasso)

✗ Drawbacks: Assumes linear relationships which rarely exist in markets, sensitive to outliers, cannot capture complex interactions, poor with non-stationary data, limited predictive power for volatile markets

Tree-Based

Decision Trees

Creates a tree-like model of decisions based on feature thresholds. For trading, it might split on rules like "If RSI > 70 AND volume > average, then SELL". Highly interpretable and forms the foundation for more powerful ensemble methods.

✓ Advantages: Easy to visualize and interpret, no feature scaling needed, handles non-linear relationships naturally, captures feature interactions, can model complex decision boundaries, works with mixed data types

✗ Drawbacks: Prone to overfitting without pruning, unstable (small data changes create different trees), not ideal for extrapolation, biased toward features with many levels, poor with imbalanced datasets

Support Vector Machine

SVM (Support Vector Machines)

Finds the optimal hyperplane that separates different market states (bull/bear, buy/sell signals). Using kernel tricks, SVMs can model complex non-linear boundaries, making them excellent for classification tasks like predicting whether a stock will rise or fall.

✓ Advantages: Excellent for binary classification, effective in high-dimensional spaces, robust against overfitting with proper regularization, kernel trick allows non-linear decision boundaries, works well with limited data

✗ Drawbacks: Computationally expensive for large datasets, requires careful hyperparameter tuning, difficult to interpret, sensitive to feature scaling, slow training time, choosing right kernel is challenging

Ensemble Learning

Random Forest

Combines hundreds of decision trees trained on random subsets of data and features. Each tree "votes" on the prediction, reducing overfitting and improving accuracy. Widely used in trading for classification and regression tasks due to its robustness and reliability.

✓ Advantages: Reduces overfitting compared to single trees, provides feature importance rankings, handles missing data well, works out-of-the-box with minimal tuning, robust to outliers, parallelizable for speed

✗ Drawbacks: Less interpretable than single tree, can be memory intensive, slower predictions than simpler models, may not capture temporal dependencies well, requires many trees for stability

Gradient Boosting

XGBoost (Extreme Gradient Boosting)

Industry-standard boosting algorithm that builds trees sequentially, with each new tree correcting errors from previous ones. Dominates Kaggle competitions and is heavily used by quantitative hedge funds. Offers superior performance on tabular financial data compared to most deep learning approaches.

✓ Advantages: State-of-the-art performance on structured data, fast training with optimized implementation, built-in regularization prevents overfitting, handles missing values, feature importance, works exceptionally well for trading signals

✗ Drawbacks: Many hyperparameters to tune, can overfit if not properly regularized, sequential training (not parallelizable), requires careful feature engineering, computationally expensive for very large datasets

Deep Learning

LSTM (Long Short-Term Memory) Networks

A specialized recurrent neural network designed for time-series data. LSTMs excel at learning patterns in sequential stock price movements, capturing long-term dependencies that traditional models miss. Used by sophisticated quant funds for price prediction and pattern recognition.

✓ Advantages: Excellent for sequential/time-series data, captures long-term dependencies, learns complex patterns automatically, handles variable-length sequences, can model temporal dynamics in markets

✗ Drawbacks: Requires massive amounts of data, computationally expensive to train, black-box predictions are hard to interpret, prone to overfitting, needs extensive hyperparameter tuning, requires GPU for practical use

💡 Pro Tip: Most successful trading algorithms start with Linear Regression and XGBoost before jumping to deep learning. These simpler models are faster to iterate, easier to debug, and often outperform complex neural networks when properly engineered. Always start simple and add complexity only when needed.

⚠️ Important Note: Machine learning trading strategies require extensive backtesting, proper validation, and robust risk management. Past performance does not guarantee future results. Always paper trade and thoroughly validate before deploying with real capital.

Investment Caculator