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import numpy as np
import pandas as pd
import torch
class Environment:
def __init__(self, data, history_t=8, history=[0.1, 0.2, -0.1, -0.2, 0., 0.5, 0.9], state_size=9):
self.data = data
self.history = history
self.history_t = history_t
self.state_size = state_size
self.cost_rate = 0.0001
self.reset()
def reset(self):
self.t = 0
self.done = False
self.profits = 0
self.position_value = 0.
self.history = self.history[:self.state_size - 1]
return [self.position_value] + self.history # obs
def step(self, act):
reward = 0
# act = 0: stay, act > 0: buy, act < 0: sell
#Additive profits
cost_amount = np.abs(act-self.position_value)
Zt = self.data.iloc[self.t, :]['Close'] - self.data.iloc[(self.t-1), :]['Close']
reward = (self.position_value * Zt) - (self.cost_rate * cost_amount)
self.profit = self.position_value * Zt
self.profits += self.profit
# set next time
self.t += 1
self.position_value = act
self.history.pop(0)
self.history.append(self.data.iloc[self.t, :]['Close'] - self.data.iloc[(self.t-1), :]['Close']) # the price being traded
self.position_value = self.position_value.item()
return [self.position_value] + self.history, reward, self.done # obs, reward, done
if __name__ == "__main__":
data = pd.read_csv('./data/EURUSD_Candlestick_1_M_BID_01.01.2021-04.02.2023.csv')
# data['Local time'] = pd.to_datetime(data['Local time'])
data = data.set_index('Local time')
print(data.index.min(), data.index.max())
date_split = '19.09.2022 17:55:00.000 GMT-0500'
train = data[:date_split]
test = data[date_split:]
print(train.head(10))
history = []
for i in range(1, 9):
c = train.iloc[i, :]['Close'] - train.iloc[i-1, :]['Close']
history.append(c)
env = Environment(train, history=history)
print(env.reset())
for _ in range(9, 12):
pact = np.random.randint(3)
print(env.step(pact)[1])
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