FinTrade Engine (C++)

A modular algorithmic trading backtesting system written in C++ that simulates trading strategies on historical price data and evaluates their performance using financial metrics.

This project demonstrates how quantitative trading strategies can be tested before deploying them in real markets.

---

Overview

FinTrade Engine reads historical market price data, applies a trading strategy, executes simulated trades, tracks portfolio performance, and produces analytics such as profit, win rate, drawdown, and equity curves.

The system replicates the typical research pipeline used in algorithmic trading and quantitative finance.

---

Key Features

• Historical market data ingestion
• Moving Average Crossover trading strategy
• Simulated portfolio and trade execution
• Backtesting framework
• Strategy performance analytics
• Maximum drawdown calculation
• Equity curve tracking
• Export of performance data to CSV
• Equity curve visualization using Python

---

System Workflow

Historical Price Data
        ↓
Trading Strategy Evaluation
        ↓
Buy / Sell Signal Generation
        ↓
Trade Execution Simulation
        ↓
Portfolio Update
        ↓
Performance Analytics
        ↓
Equity Curve Generation

---

Trading Strategy

This project implements a Moving Average Crossover Strategy.

Signal Rules

Short MA > Long MA → BUY
Short MA < Long MA → SELL
Short MA = Long MA → HOLD

Where:

• Short MA = Moving average of the last 3 prices
• Long MA = Moving average of the last 5 prices

The crossover of these averages helps detect market trends.

---

Example Trading Output

BUY EXECUTED
TEST x 5 @ ₹187

SELL EXECUTED
TEST x 5 @ ₹200
Final Trade Profit: ₹65

------ Portfolio ------
Balance: ₹100065

Positions:
TEST : 0 shares
-----------------------

------ Strategy Report ------
Total Trades : 1
Total Profit : ₹65
Win Rate     : 100%
Avg Profit   : ₹65
Max Drawdown : ₹10
-----------------------------

---

Screenshots

Trading Simulation Output

---

Strategy Equity Curve

Performance Metrics

The system evaluates strategies using several metrics:

Metric	Description
Total Trades	Number of completed trades
Total Profit	Overall strategy profit
Win Rate	Percentage of profitable trades
Average Profit	Mean profit per trade
Max Drawdown	Largest drop from peak equity

These metrics help assess strategy profitability and risk.

---

Equity Curve

The system tracks portfolio value over time and exports it to:

equity_curve.csv

Example:

Step,Equity
1,100000
2,100000
3,100000
4,100000
5,100000
6,100015
7,100005
8,100020
9,100040
10,100065

This data can be visualized to analyze strategy performance over time.

---

Equity Curve Visualization

A Python script is included to plot the equity curve.

Install dependencies

pip3 install pandas matplotlib

Run visualization

python3 scripts/plot_equity.py

This generates a performance chart of the trading strategy.

---

Project Architecture

FinTrade Engine
│
├── BacktestEngine
│   Loads historical price data
│
├── TradingEngine
│   Generates buy/sell signals using strategy logic
│
├── PortfolioManager
│   Simulates portfolio trades and tracks balance
│
├── AnalyticsEngine
│   Calculates performance metrics and equity curves
│
└── Main Program
    Orchestrates the full workflow

---

Project Structure

FinTrade-Engine
│
|__ Screenshots
|   Includes demo screenshots
|
├── src
│   ├── main.cpp
│   ├── MarketDataEngine.cpp
│   ├── TradingEngine.cpp
│   ├── PortfolioManager.cpp
│   ├── BacktestEngine.cpp
│   └── AnalyticsEngine.cpp
│
├── include
│   ├── MarketDataEngine.h
│   ├── TradingEngine.h
│   ├── PortfolioManager.h
│   ├── BacktestEngine.h
│   └── AnalyticsEngine.h
│
├── scripts
│   └── plot_equity.py
│
├── prices.csv
├── equity_curve.csv
└── README.md

---

Build Instructions

Compile the project:

g++ -std=c++17 src/*.cpp -o fintrade -lcurl -pthread

Run the program:

./fintrade

---

Technologies Used

• C++17
• Standard Template Library (STL)
• File I/O for historical data processing
• Python (Matplotlib & Pandas) for visualization

---

Future Improvements

Possible extensions of the project include:

• Sharpe ratio calculation
• Support for multiple trading strategies
• Real-time market data integration
• Strategy parameter optimization
• Strategy comparison framework
• GUI-based visualization
• Machine learning based trading signals

---

Learning Outcomes

This project demonstrates:

• System design in C++
• Algorithmic trading concepts
• Financial data analysis
• Backtesting framework development
• Portfolio simulation
• Risk and performance evaluation

---

Author

Ashutosh Upreti

Computer Science Student Interested in C++, system design, and quantitative trading systems.