Spaces:
Runtime error
Runtime error
| 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): | |
| # if self.t == len(self.data) - 1: | |
| # return self.history + [0], 0, False | |
| 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]) | |