Barcola — Quantitative Trading Toolkit in C++17

A modular quantitative analysis and backtesting framework built from scratch in modern C++. No external trading library dependencies.

Building

cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --parallel
ctest --test-dir build --output-on-failure

Requirements: C++17 compiler, CMake 3.16+, libcurl

Project Structure

src/
├── core/           PricePoint, PriceHistory (CSV + Yahoo Finance), time utilities
├── indicators/     SMA, EMA, RSI, Bollinger Bands
├── analysis/       Mean reversion, correlation, market data analysis
├── risk/           Position sizing, slippage modeling, dynamic hedging
└── simulation/     Monte Carlo simulation
tests/              Google Test suite (14 test files, 60+ test cases)
data/               Static CSV fixtures for reproducible testing

Modules

Module	Description
Core	OHLCV data structures, Yahoo Finance API integration, CSV import/export, date utilities
Indicators	Simple & exponential moving averages, RSI, Bollinger Bands
Analysis	Mean reversion detection, Pearson correlation, volatility & trend analysis
Risk	Position sizing (risk-based & max-loss), slippage modeling with volume tiers, dynamic hedging
Simulation	Monte Carlo simulation with dual-SMA crossover strategy

Data Loading

#include "core/price_history.h"

barcola::PriceHistory history("AAPL");

// From CSV (for reproducible testing and backtesting)
history.loadFromCsv("data/AAPL_2023_sample.csv");

// From Yahoo Finance API (live data)
history.fetchHistoricalData("2023-01-01", "2023-12-31", "1d");

// Save fetched data for later use
history.saveToCsv("data/aapl_2023.csv");

Quick Example

#include "core/price_history.h"
#include "indicators/rsi.h"
#include "analysis/correlation.h"

using namespace barcola;

PriceHistory aapl("AAPL");
aapl.loadFromCsv("data/AAPL_2023_sample.csv");

// Calculate RSI
std::vector<double> prices;
for (const auto& dp : aapl.getDataPoints()) {
    prices.push_back(dp.getClosing());
}
double rsi = calculateRSI(prices, 14);

// Correlation between two assets
PriceHistory msft("MSFT");
msft.loadFromCsv("data/MSFT_2023_sample.csv");
CorrelationAnalysis corr(aapl, msft);
double correlation = corr.calculateCorrelation();

Roadmap

• Phase 1: Foundation hardening (CMake, tests, CI, bug fixes, CSV support)
• Phase 2: Backtesting engine (strategy interface, engine, performance metrics)
• Phase 3: Mean reversion backtest with documented results
• Phase 4: Second strategy (dual MA crossover) + comparison
• Phase 5: Polish, visualizations, results-first README