Strategy Prototyping: From Python to C++

Why this screen matters

• You will normally prototype quickly in Python (pandas/NumPy) to validate strategy logic. When the inner loop becomes a bottleneck, you migrate just that hotspot to C++ for speed and deterministic performance.
• Think of Python as your whiteboard sketch and C++ as the high-performance court — the plays are the same, but execution is faster and more precise.

High-level workflow (ASCII diagram)

Python prototype (fast iterate) ---> Profile (cProfile/line_profiler/pyinstrument) ---> Identify hot function(s) ---> Reimplement hot function(s) in C++ (pybind11 or RPC) ---> Integrate & benchmark ---> Deploy

Analogy for beginners (basketball)

• Prototype in Python = film-study with Kobe Bryant highlights. You find the play that scores most often.
• Hotspot = the quick cut that wins the game (micro-ops inside your loop).
• Migrating to C++ = sending your best shooter to the court who always hits under pressure.

Concrete tips for a beginner in C++, Python, Java, C, JS

• Prototype quickly in Python: use small, readable code & synthetic ticks (lists of (timestamp, price, size)).
• Profile early: find the exact function (not the file) that takes most time—funcA doing rolling sums? that's your candidate.
• Reimplement minimally: keep the same inputs/outputs. Start with a small, well-tested C++ function that computes e.g. a rolling average or VWAP.
• Expose to Python: start with pybind11 (a thin wrapper). If deployment needs process isolation, use an RPC boundary (nanomsg, gRPC, or raw TCP).

What to migrate (common hotspots)

• Inner loops that process every tick (aggregation, feature extraction, order decision logic).
• Parsing heavy binary formats (market ITCH/OUCH) — low-level parsers in C++ can drastically reduce CPU and copies.
• Memory-allocation hot spots — re-use buffers in C++ and avoid per-tick malloc.

Quick checklist before migrating

• Can I vectorize this in NumPy? If yes, you may not need C++.
• Is the function called millions of times per second? If yes, it's a prime candidate.
• Are allocations and copies dominating CPU? Move to a C++ ring buffer.

Mini-exercise (what the C++ code below demonstrates)

• Generates a stream of synthetic prices (deterministic seed so results are reproducible).
• Implements two ways to compute a rolling simple moving average (SMA):
  • naive_sma: recompute the sum each tick (like a straightforward Python loop).
  • incremental_sma: maintain an incremental sum (how you'd implement it in C++ for speed).
• Compares timings so you can see why migrating the inner loop matters.

Try these challenges after running the example

• Change the window size (WINDOW) and re-run. How does the speed gap evolve?
• Replace the random tick generator with a small histogram or real CSV replay (simulate Kobe Bryant moments by injecting spikes).
• Wrap the incremental_sma in pybind11 and call it from Python for a real prototype -> production path.

Now run the C++ example below (it prints timings and a few sample buy decisions). Then try the challenges!

xxxxxxxxxx

}

#include <iostream>

#include <vector>

#include <numeric>

#include <chrono>

#include <random>

#include <deque>

#include <string>

​

using namespace std;

using clock_t = chrono::high_resolution_clock;

​

// Small struct to look like a tick: (timestamp, price, size)

struct Tick { long long ts; double price; double size; };

​

// Naive SMA: recompute sum every time (like a simple Python loop over a list slice)

vector<double> naive_sma(const vector<Tick>& ticks, size_t window) {

  vector<double> out;

  out.reserve(ticks.size());

  for (size_t i = 0; i < ticks.size(); ++i) {

    if (i + 1 < window) { out.push_back(0.0); continue; }

    double s = 0.0;

    for (size_t j = i + 1 - window; j <= i; ++j) s += ticks[j].price;

    out.push_back(s / double(window));

  }

  return out;

}

​

// Incremental SMA: maintain running sum (the typical C++ hotspot implementation)

vector<double> incremental_sma(const vector<Tick>& ticks, size_t window) {