#ifndef DECIDER_H_
#define DECIDER_H_

#include "helpers.h"
#include "network.h"
#include <iterator>
#include <iostream>

class decider {
public:
    double   start_money;
    unsigned start_stock;

    virtual int decide(double price, double money, unsigned stock) = 0;
    virtual void reset() = 0;

    virtual ~decider() { }
};

class macd_decider {
private:
    std::size_t limit;

protected:
    buffer<double> prices;
    buffer<double> macd;
    buffer<double> signal;

public:
    macd_decider(std::size_t limit = 1000);

    virtual void process(double price);
    virtual void reset();
};

template <int p, int m, int s, int ...layers>
class neural_decider : public decider, macd_decider {
public:
    using network_t = network<double, p+m+s+2, layers..., 2>;
    network_t network;

    double   start_money;
    unsigned start_stock;

    neural_decider() : network(), macd_decider() { }
    neural_decider(typename network_t::normalizer_t normalizer) 
        : network(normalizer), macd_decider()  { }

    virtual int decide(double price, double money, unsigned stock)
    {
        process(price);

        auto input = prepare(money, stock);
        auto result = network.evaluate(input);

        double buy  = result.get(0, 0);
        double sell = result.get(1, 0);

        double amount = (buy - sell - .5)/1.5;

        return abs(buy - sell) <= .5 ? 0 : amount * start_stock / 10;
    }

    virtual void reset()
    {
        this->reset();
    }

    virtual ~neural_decider() { }

private:
    typename network_t::input prepare(double money, unsigned stock)
    {
        vector<double, 2> state = { 
            { (money - start_money) / start_money }, 
            { (double)stock / this->start_stock }
        };

        vector<double, p> prices;
        vector<double, m+s> tech;

        for (int j = 0; j < p; j++) {
            prices.set(j, 0, this->prices[j]);
        }
        
        int i = 0;
        for (int j = 0; j < m; j++, i++) {
            tech.set(i, 0, macd[j]);
        }

        for (int j = 0; j < s; j++, i++) {
            tech.set(i, 0, signal[j]);
        }

        // prices are normalized in their domain
        prices = normalize(prices);
        // analytic data is normalized in its own domain
        tech   = normalize(tech);

        auto concated = concat(prices, tech);

        return concat(concated, state);
    }
};

#endif