ForexBacktestingPython/FXBacktestingPython at main · GeniusMathematicsConsultants/ForexBacktestingPython · GitHub

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import minimize

#data = pd.read_csv("GBPUSD.txt")
idx = 0
Names = ["GBPUSD", "EURUSD", "AUDUSD", "XAUUSD"]
Name = Names[idx]
data = pd.read_csv(Name + ".txt")
#data = data.iloc[::-1] #reverses order of dates

# Filter for daily data only
data = data.drop_duplicates(subset=['<DTYYYYMMDD>'], keep='last')

close = np.array(data["<CLOSE>"])
#close = close[:3389]

def moving_avg(close, index, days, alpha):
    # float values allowed for days for use with optimization routines.
    partial = days - np.floor(days)
    days = int(np.floor(days))

    weights = [alpha**i for i in range(days)]
    av_period = list(close[max(index - days + 1, 0): index+1])

    if partial > 0:
        weights = [alpha**(days)*partial] + weights
        av_period = [close[max(index - days, 0)]] + av_period

    return np.average(av_period, weights=weights)


def calculate_strategy(close, short_days, long_days, alpha, start_offset, threshold):
    strategy = [0]*(len(close) - start_offset)
    short = [0]*(len(close) - start_offset)
    long = [0]*(len(close) - start_offset)
    boughtorsold = 1
    for i in range(0, len(close) - start_offset):
        short[i] = moving_avg(close, i + start_offset, short_days, alpha)
        long[i] = moving_avg(close, i + start_offset, long_days, alpha)
        if short[i] >= long[i]*(1+threshold) and boughtorsold != 1:
            boughtorsold = 1
            strategy[i] = 1
        if short[i] <= long[i]*(1-threshold) and boughtorsold != -1:
            boughtorsold = -1
            strategy[i] = -1
    return (strategy, short, long)


def price_strategy(strategy, close, short_days, long_days, alpha, start_offset):
    cash = 1/close[start_offset]  # Start with one unit of CCY2, converted into CCY1
    bought = 1
    for i in range(0, len(close) - start_offset):
        #print(strategy[i])
        if strategy[i] == 1:
            cash = cash/close[i + start_offset]
            bought = 1
        if strategy[i] == -1:
            cash = cash*close[i + start_offset]
            bought = -1
    # Sell at end
    if bought == 1:
        cash = cash*close[-1]
    #if bought == -1:
    #    cash = cash - close[-1]
    return cash

def graph_strategy(close, strategy, short, long, start_offset):
    x = list(range(0, len(close) - start_offset))
    plt.figure(0)
    plt.plot(x, close[start_offset:], label = Name)
    plt.plot(x, short, label = "short_av")
    plt.plot(x, long, label = "long_av")
    buyidx = []
    sellidx = []
    for i in range(len(strategy)):
        if strategy[i] == 1:
            buyidx.append(i)
        elif strategy[i] == -1:
            sellidx.append(i)
    marker_height = (1+0.1)*min(close) - 0.1*max(close)
    plt.scatter(buyidx, [marker_height]*len(buyidx), label = "Buy", marker="|")
    plt.scatter(sellidx, [marker_height]*len(sellidx), label = "Sell", marker="|")
    plt.title('Moving average crossover')
    plt.xlabel('Timestep')
    plt.ylabel('Price')
    plt.legend()

def plot_param(x, close, start_offset, param_index, param_values):
    profit = []
    x2 = x.copy()
    for value in param_values:
        x2[param_index] = value
        short_days = x2[0]
        long_days = x2[1]
        alpha = x2[2]
        threshold = x2[3]
        (strat, short, long) =  calculate_strategy(close, short_days, long_days, alpha, start_offset, threshold)
        profit.append(price_strategy(strat, close, short_days, long_days, alpha, start_offset) - 1)
    plt.figure(param_index+1)
    param_names = ["short_days", "long_days", "alpha", "threshold"]
    name = param_names[param_index]
    plt.title('Strategy profit vs ' + name)
    plt.xlabel(name)
    plt.ylabel('Profit')
    plt.plot(param_values, profit, label = "Profit")

def evaluate_params(x, close, start_offset):
    short_days = x[0]
    long_days = x[1]
    alpha = x[2]
    threshold = x[3]
    (strat1, short, long) =  calculate_strategy(close, short_days, long_days, alpha, start_offset, threshold)
    profit = price_strategy(strat1, close, short_days, long_days, alpha, start_offset)
    return -profit #Since we minimise

#Initial strategy parameters.
short_days = 30
long_days = 300
alpha = 0.95
start_offset = 300
threshold = 0.02#0.01

#short_days = 10marker_height
#long_days = 30
#alpha = 0.75
#alpha = 0.92
#threshold = 0.02

x = [short_days, long_days, alpha, threshold]

#Price strategy
(strat1, short, long) =  calculate_strategy(close, short_days, long_days, alpha, start_offset, threshold)
profit = price_strategy(strat1, close, short_days, long_days, alpha, start_offset)
print("Strategy profit is: " + str(profit - 1))
print("Buy and hold profit is: " + str(close[-1]/close[start_offset] - 1))

#Graph strategy
graph_strategy(close, strat1, short, long, start_offset)

#Graph parameter dependence
plot_param(x, close, start_offset, 2, np.arange(0.7, 1, 0.02))
plot_param(x, close, start_offset, 3, np.arange(0.01, 0.1, 0.001))
plot_param(x, close, start_offset, 0, np.arange(20, long_days, 10))
plot_param(x, close, start_offset, 1, np.arange(short_days, 300, 10))