maker.cpp

#include <iostream>
#include <cmath>
#include <random>
#include <chrono>
#include <vector>
#include <algorithm>
#include <fstream>
#include <sstream>
#include <torch/torch.h>
#include <torch/script.h>

using namespace std;

const double INITIAL_PRICE = 100.0;
const double SPREAD = 1.0;
const double RISK_FREE_RATE = 0.05;

struct Option {
    string symbol;
    double strike;
    double expiry;
    double price;
    int quantity;
};

struct Future {
    string symbol;
    double expiry;
    double price;
    int quantity;
};

double generateRandomNumber(double mean, double std_dev) {
    unsigned seed = chrono::system_clock::now().time_since_epoch().count();
    default_random_engine generator(seed);
    normal_distribution<double> distribution(mean, std_dev);
    return distribution(generator);
}

void calculateBidAsk(double mid_price, double& bid_price, double& ask_price) {
    double half_spread = SPREAD / 2.0;
    bid_price = mid_price - half_spread;
    ask_price = mid_price + half_spread;
}

double blackScholes(double S, double K, double T, double r, double sigma, bool isCall) {
    double d1 = (log(S / K) + (r + 0.5 * sigma * sigma) * T) / (sigma * sqrt(T));
    double d2 = d1 - sigma * sqrt(T);

    if (isCall) {
        double call_price = S * exp(-r * T) * N(d1) - K * exp(-r * T) * N(d2);
        return call_price;
    } else {
        double put_price = K * exp(-r * T) * N(-d2) - S * exp(-r * T) * N(-d1);
        return put_price;
    }
}

double N(double x) {
    return 0.5 * (1 + erf(x / sqrt(2)));
}

void handleBuyOrder(const Option& option, double bid_price, double& optionsPnL) {
    double pnl = (option.price - bid_price) * option.quantity;
    optionsPnL += pnl;
}

void handleSellOrder(const Option& option, double ask_price, double& optionsPnL) {
    double pnl = (ask_price - option.price) * option.quantity;
    optionsPnL += pnl;
}

void handleBuyOrder(const Future& future, double bid_price, double& futuresPnL) {
    double pnl = (future.price - bid_price) * future.quantity;
    futuresPnL += pnl;
}

void handleSellOrder(const Future& future, double ask_price, double& futuresPnL) {
    double pnl = (ask_price - future.price) * future.quantity;
    futuresPnL += pnl;
}

torch::Tensor loadTrainingData(const string& file_path) {
    ifstream file(file_path);
    vector<vector<float>> data;
    string line;
    while (getline(file, line)) {
        istringstream iss(line);
        vector<float> row;
        float val;
        while (iss >> val) {
            row.push_back(val);
        }
        data.push_back(row);
    }
    file.close();

    auto options_data = torch::from_blob(data.data(), {data.size(), data[0].size()});
    return options_data;
}

torch::nn::Sequential trainVolatilityModel(torch::Tensor training_data) {
    torch::manual_seed(0);
    auto dtype = torch::kFloat32;
    auto device = torch::kCPU;

    int input_size = training_data.size(1);
    int hidden_size = 32;
    int output_size = 1;
    int num_layers = 2;

    torch::nn::Sequential model(
        torch::nn::LSTM(torch::nn::LSTMOptions(input_size, hidden_size).num_layers(num_layers)),
        torch::nn::Linear(hidden_size, output_size)
    );
    model->to(dtype).to(device);

    torch::optim::Adam optimizer(model->parameters(), torch::optim::AdamOptions(0.001));

    int num_epochs = 10;
    int seq_length = 5;

    for (int epoch = 0; epoch < num_epochs; ++epoch) {
        for (int i = 0; i < training_data.size(0) - seq_length; ++i) {
            torch::Tensor inputs = training_data.narrow(0, i, seq_length).unsqueeze(1);
            torch::Tensor targets = training_data.narrow(0, i + seq_length, 1);

            inputs = inputs.to(dtype).to(device);
            targets = targets.to(dtype).to(device);

            optimizer.zero_grad();

            torch::Tensor outputs = model->forward(inputs);

            torch::Tensor loss = torch::mse_loss(outputs, targets);
            loss.backward();

            optimizer.step();
        }
    }

    return model;
}

double predictVolatility(const torch::nn::Sequential& model, double current_price) {
    torch::NoGradGuard no_grad;
    model->eval();

    int input_size = model->named_parameters()["0.weight"].size(1);
    torch::Tensor input = torch::empty({1, input_size});
    input[0][0] = current_price;

    torch::Tensor output = model->forward(input.unsqueeze(1));
    double predicted_volatility = output[0][0].item<double>();

    return predicted_volatility;
}

int main() {
    double current_price = INITIAL_PRICE;

    vector<Option> options{
        {"AAPL", 120.0, 0.25, 0.0, 0},
        {"GOOG", 2000.0, 0.5, 0.0, 0},
        {"MSFT", 150.0, 0.75, 0.0, 0},
        {"AMZN", 3000.0, 1.0, 0.0, 0}
    };

    vector<Future> futures{
        {"CL", 1.0, 0.0, 0},
        {"ES", 1.0, 0.0, 0},
        {"NQ", 1.0, 0.0, 0},
        {"GC", 1.0, 0.0, 0}
    };

    double optionsPnL = 0.0;
    double futuresPnL = 0.0;

    torch::Tensor training_data = loadTrainingData("volatility_data.csv");
    torch::nn::Sequential volatilityModel = trainVolatilityModel(training_data);

    for (int i = 1; i <= 10; ++i) {
        double price_change = generateRandomNumber(0.0, predictVolatility(volatilityModel, current_price));

        double mid_price = current_price + price_change;

        double bid_price, ask_price;
        calculateBidAsk(mid_price, bid_price, ask_price);

        current_price = mid_price;

        for (auto& option : options) {
            option.price = blackScholes(current_price, option.strike, option.expiry, RISK_FREE_RATE, predictVolatility(volatilityModel, current_price), true


    for (auto& future : futures) {
        future.price = current_price;
    }

    // Handle buy and sell orders for options
    for (auto& option : options) {
        if (option.quantity > 0) {
            handleSellOrder(option, ask_price, optionsPnL);
        } else if (option.quantity < 0) {
            handleBuyOrder(option, bid_price, optionsPnL);
        }
    }

    // Handle buy and sell orders for futures
    for (auto& future : futures) {
        if (future.quantity > 0) {
            handleSellOrder(future, ask_price, futuresPnL);
        } else if (future.quantity < 0) {
            handleBuyOrder(future, bid_price, futuresPnL);
        }
    }
}

cout << "Options PnL: " << optionsPnL << endl;
cout << "Futures PnL: " << futuresPnL << endl;

return 0;