Backtesting and Simulation Environment

Design goal: build reproducible backtests and a small market-replay + synthetic exchange so you can validate strategies offline. This screen gives a compact mental model and a tiny C++ playground you can run and edit — perfect for beginners in C++, Python, Java, C, or JS who want to see the whole pipeline.

Why this matters

• Reproducible backtests let you compare strategy changes deterministically (same ticks -> same trades).
• A market replay engine feeds your strategy with historical ticks and sequence numbers so you can test gap-handling.
• A synthetic exchange / matching engine implements a minimal order book to check execution logic and slippage.

Core components (ASCII diagram)

Historical ticks (file/array) --> Market Replay --> Strategy --> Order Gateway --> Matching Engine --> Trades/Logs

Key concepts

• Tick = (timestamp, seq, price, size). Use seq to detect dropped packets / gaps.
• Replayer: emits ticks in order (optionally with controllable timing) so your algorithm sees the same stream each run.
• Matching engine: a tiny order book that matches buys vs sells deterministically — good for unit tests.
• Deterministic randomness: if you must use randomness, seed it (e.g., std::mt19937 rng(42)).

Analogy for beginners: think of the replay as a basketball practice tape — you can replay Kobe Bryant's moves frame-by-frame to refine your passes (orders) and reactions (strategy). Replace Kobe Bryant with your favorite player and watch how changing one pass timing changes the play outcome.

What the included C++ demo does

• Simulates a short tick stream with an intentional sequence gap to show detection.
• Runs a trivial strategy: place a limit buy when price drops below a threshold.
• Implements a tiny matching engine that prints executed trades and remaining order-book state.

Try these challenges

• Change the favorite player string (currently Kobe Bryant) to yours and print it with each trade.
• Add latency emulation: when matching, add a small sleep to simulate network delays and measure missed fills.
• Reimplement the replay part in Python (use struct.unpack and lists) and compare code size and ergonomics.
• Add VWAP calculation for each simulated trade batch and log it.

Now run the C++ example below and then try modifying it: change tick prices, insert another sequence gap, or make the strategy more aggressive.

1// see the separate code block below — run and edit main.cpp

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}

#include <iostream>

#include <vector>

#include <string>

#include <algorithm>

​

using namespace std;

​

struct Tick {

  int timestamp;

  int seq;

  double price;

  int size;

};

​

struct Order {

  int id;

  bool is_buy;

  double price; // limit price

  int size;

};

​

struct Trade {

  int buy_id;

  int sell_id;

  double price;

  int size;

};

​

// Very small, naive matching: match incoming order with opposite book