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StockTradingEnv.py
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StockTradingEnv.py
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import os
import numpy as np
import numpy.random as rd
import pandas as pd
import torch
from functorch import vmap
class StockTradingEnv:
def __init__(self, initial_amount=1e6, max_stock=1e2, cost_pct=1e-3, gamma=0.99,
beg_idx=0, end_idx=1113):
self.df_pwd = './elegantrl/envs/China_A_shares.pandas.dataframe'
self.npz_pwd = './elegantrl/envs/China_A_shares.numpy.npz'
self.close_ary, self.tech_ary = self.load_data_from_disk()
self.close_ary = self.close_ary[beg_idx:end_idx]
self.tech_ary = self.tech_ary[beg_idx:end_idx]
# print(f"| StockTradingEnv: close_ary.shape {self.close_ary.shape}")
# print(f"| StockTradingEnv: tech_ary.shape {self.tech_ary.shape}")
self.max_stock = max_stock
self.cost_pct = cost_pct
self.reward_scale = 2 ** -12
self.initial_amount = initial_amount
self.gamma = gamma
# reset()
self.day = None
self.rewards = None
self.total_asset = None
self.cumulative_returns = 0
self.if_random_reset = True
self.amount = None
self.shares = None
self.shares_num = self.close_ary.shape[1]
amount_dim = 1
# environment information
self.env_name = 'StockTradingEnv-v2'
self.state_dim = self.shares_num + self.close_ary.shape[1] + self.tech_ary.shape[1] + amount_dim
self.action_dim = self.shares_num
self.if_discrete = False
self.max_step = self.close_ary.shape[0] - 1
self.target_return = +np.inf
def reset(self):
self.day = 0
if self.if_random_reset:
self.amount = self.initial_amount * rd.uniform(0.9, 1.1)
self.shares = (np.abs(rd.randn(self.shares_num).clip(-2, +2)) * 2 ** 6).astype(int)
else:
self.amount = self.initial_amount
self.shares = np.zeros(self.shares_num, dtype=np.float32)
self.rewards = []
self.total_asset = (self.close_ary[self.day] * self.shares).sum() + self.amount
return self.get_state()
def get_state(self):
state = np.hstack((np.tanh(np.array(self.amount * 2 ** -16)),
self.shares * 2 ** -9,
self.close_ary[self.day] * 2 ** -7,
self.tech_ary[self.day] * 2 ** -6,))
return state
def step(self, action):
self.day += 1
action = action.copy()
action[(-0.1 < action) & (action < 0.1)] = 0
action_int = (action * self.max_stock).astype(int)
# actions initially is scaled between -1 and 1
# convert into integer because we can't buy fraction of shares
for index in range(self.action_dim):
stock_action = action_int[index]
adj_close_price = self.close_ary[self.day, index] # `adjcp` denotes adjusted close price
if stock_action > 0: # buy_stock
delta_stock = min(self.amount // adj_close_price, stock_action)
self.amount -= adj_close_price * delta_stock * (1 + self.cost_pct)
self.shares[index] += delta_stock
elif self.shares[index] > 0: # sell_stock
delta_stock = min(-stock_action, self.shares[index])
self.amount += adj_close_price * delta_stock * (1 - self.cost_pct)
self.shares[index] -= delta_stock
total_asset = (self.close_ary[self.day] * self.shares).sum() + self.amount
reward = (total_asset - self.total_asset) * self.reward_scale
self.rewards.append(reward)
self.total_asset = total_asset
done = self.day == self.max_step
if done:
reward += 1 / (1 - self.gamma) * np.mean(self.rewards)
self.cumulative_returns = total_asset / self.initial_amount * 100 # todo
state = self.get_state()
return state, reward, done, {}
def load_data_from_disk(self, tech_id_list=None):
tech_id_list = [
"macd", "boll_ub", "boll_lb", "rsi_30", "cci_30", "dx_30", "close_30_sma", "close_60_sma",
] if tech_id_list is None else tech_id_list
if os.path.exists(self.npz_pwd):
ary_dict = np.load(self.npz_pwd, allow_pickle=True)
close_ary = ary_dict['close_ary']
tech_ary = ary_dict['tech_ary']
elif os.path.exists(self.df_pwd): # convert pandas.DataFrame to numpy.array
df = pd.read_pickle(self.df_pwd)
tech_ary = []
close_ary = []
df_len = len(df.index.unique()) # df_len = max_step
for day in range(df_len):
item = df.loc[day]
tech_items = [item[tech].values.tolist() for tech in tech_id_list]
tech_items_flatten = sum(tech_items, [])
tech_ary.append(tech_items_flatten)
close_ary.append(item.close)
close_ary = np.array(close_ary)
tech_ary = np.array(tech_ary)
np.savez_compressed(self.npz_pwd, close_ary=close_ary, tech_ary=tech_ary, )
else:
error_str = f"| StockTradingEnv need {self.df_pwd} or {self.npz_pwd}" \
f"\n download the following files and save in `.`" \
f"\n https://github.com/Yonv1943/Python/blob/master/scow/China_A_shares.numpy.npz" \
f"\n https://github.com/Yonv1943/Python/blob/master/scow/China_A_shares.pandas.dataframe"
raise FileNotFoundError(error_str)
return close_ary, tech_ary
'''function for vmap'''
def _inplace_amount_shares_when_buy(amount, shares, stock_action, close, cost_pct):
stock_delta = torch.min(stock_action, torch.div(amount, close, rounding_mode='floor'))
amount -= close * stock_delta * (1 + cost_pct)
shares += stock_delta
return torch.zeros(1)
def _inplace_amount_shares_when_sell(amount, shares, stock_action, close, cost_rate):
stock_delta = torch.min(-stock_action, shares)
amount += close * stock_delta * (1 - cost_rate)
shares -= stock_delta
return torch.zeros(1)
class StockTradingVecEnv:
def __init__(self, initial_amount=1e6, max_stock=1e2, cost_pct=1e-3, gamma=0.99,
beg_idx=0, end_idx=1113, num_envs=4, gpu_id=0):
self.df_pwd = './elegantrl/envs/China_A_shares.pandas.dataframe'
self.npz_pwd = './elegantrl/envs/China_A_shares.numpy.npz'
self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
'''load data'''
close_ary, tech_ary = self.load_data_from_disk()
close_ary = close_ary[beg_idx:end_idx]
tech_ary = tech_ary[beg_idx:end_idx]
self.close_price = torch.tensor(close_ary, dtype=torch.float32, device=self.device)
self.tech_factor = torch.tensor(tech_ary, dtype=torch.float32, device=self.device)
# print(f"| StockTradingEnv: close_ary.shape {close_ary.shape}")
# print(f"| StockTradingEnv: tech_ary.shape {tech_ary.shape}")
'''init'''
self.gamma = gamma
self.cost_pct = cost_pct
self.max_stock = max_stock
self.reward_scale = 2 ** -12
self.initial_amount = initial_amount
self.if_random_reset = True
'''init (reset)'''
self.day = None
self.rewards = None
self.total_asset = None
self.cumulative_returns = None
self.amount = None
self.shares = None
self.clears = None
self.num_shares = self.close_price.shape[1]
amount_dim = 1
'''environment information'''
self.env_name = 'StockTradingEnv-v2'
self.num_envs = num_envs
self.max_step = self.close_price.shape[0] - 1
self.state_dim = self.num_shares + self.close_price.shape[1] + self.tech_factor.shape[1] + amount_dim
self.action_dim = self.num_shares
self.if_discrete = False
'''vmap function'''
self.vmap_get_state = vmap(
func=lambda amount, shares, close, techs: torch.cat((amount, shares, close, techs)),
in_dims=(0, 0, None, None), out_dims=0)
self.vmap_get_total_asset = vmap(
func=lambda close, shares, amount: (close * shares).sum() + amount,
in_dims=(None, 0, 0), out_dims=0)
self.vmap_inplace_amount_shares_when_buy = vmap(
func=_inplace_amount_shares_when_buy, in_dims=(0, 0, 0, None, None), out_dims=0)
self.vmap_inplace_amount_shares_when_sell = vmap(
func=_inplace_amount_shares_when_sell, in_dims=(0, 0, 0, None, None), out_dims=0)
def reset(self):
self.day = 0
self.amount = torch.zeros((self.num_envs, 1), dtype=torch.float32, device=self.device) + self.initial_amount
self.shares = torch.zeros((self.num_envs, self.num_shares), dtype=torch.float32, device=self.device)
if self.if_random_reset:
rand_amount = torch.rand((self.num_envs, 1), dtype=torch.float32, device=self.device) * 0.5 + 0.75
self.amount = self.amount * rand_amount
rand_shares = torch.randn((self.num_envs, self.num_shares), dtype=torch.float32, device=self.device)
rand_shares = rand_shares.clip(-2, +2) * 2 ** 7
self.shares = self.shares + torch.abs(rand_shares).type(torch.int32)
self.rewards = list()
self.total_asset = self.vmap_get_total_asset(self.close_price[self.day], self.shares, self.amount)
return self.get_state()
def get_state(self):
return self.vmap_get_state((self.amount * 2 ** -18).tanh(),
(self.shares * 2 ** -10).tanh(),
self.close_price[self.day] * 2 ** -7,
self.tech_factor[self.day] * 2 ** -6) # state
def step(self, action):
self.day += 1
if self.day == 1:
self.cumulative_returns = 0.
# action = action.clone()
action = torch.ones_like(action)
action[(-0.1 < action) & (action < 0.1)] = 0
action_int = (action * self.max_stock).to(torch.int32)
# actions initially is scaled between -1 and 1
# convert `action` into integer as `stock_action`, because we can't buy fraction of shares
for i in range(self.num_shares):
buy_idx = torch.where(action_int[:, i] > 0)[0]
if buy_idx.shape[0] > 0:
part_amount = self.amount[buy_idx]
part_shares = self.shares[buy_idx, i]
self.vmap_inplace_amount_shares_when_buy(part_amount, part_shares, action_int[buy_idx, i],
self.close_price[self.day, i], self.cost_pct)
self.amount[buy_idx] = part_amount
self.shares[buy_idx, i] = part_shares
sell_idx = torch.where((action_int < 0) & (self.shares > 0))[0]
if sell_idx.shape[0] > 0:
part_amount = self.amount[sell_idx]
part_shares = self.shares[sell_idx, i]
self.vmap_inplace_amount_shares_when_sell(part_amount, part_shares, action_int[sell_idx, i],
self.close_price[self.day, i], self.cost_pct)
self.amount[sell_idx] = part_amount
self.shares[sell_idx, i] = part_shares
# for index in range(self.action_dim):
# stock_actions = action_int[:, index]
# close_price = self.close_price[self.day, index]
#
# # delta_stock.shape == ()
# for i in range(self.num_envs):
# if stock_actions[i] > 0: # buy_stock
# delta_stock = torch.div(self.amount[i], close_price, rounding_mode='floor')
# delta_stock = torch.min(delta_stock, stock_actions[0])
# self.amount[i] -= close_price * delta_stock * (1 + self.cost_pct)
# self.shares[i, index] = self.shares[i, index] + delta_stock
# elif self.shares[i, index] > 0: # sell_stock
# delta_stock = torch.min(-stock_actions[i], self.shares[i, index])
# self.amount[i] += close_price * delta_stock * (1 - self.cost_pct)
# self.shares[i, index] = self.shares[i, index] + delta_stock
'''random clear the inventory'''
# reset_rate = 1e-2 * self.num_shares / self.max_step
# if self.if_random_reset and (rd.rand() < reset_rate):
# env_i = rd.randint(self.num_envs)
# shares_i = rd.randint(self.num_shares)
#
# self.amount[env_i] = (self.amount[env_i] +
# self.shares[env_i, shares_i] * self.close_price[self.day, shares_i]) # not cost_pct
# self.shares[env_i, shares_i] = 0
'''get reward'''
total_asset = self.vmap_get_total_asset(self.close_price[self.day], self.shares, self.amount)
reward = (total_asset - self.total_asset).squeeze(1) * self.reward_scale # shape == (num_envs, )
self.rewards.append(reward)
self.total_asset = total_asset
'''get done and state'''
done = self.day == self.max_step
if done:
reward += torch.stack(self.rewards).mean(dim=0) * (1. / (1. - self.gamma))
self.cumulative_returns = (total_asset / self.initial_amount) * 100 # todo
self.cumulative_returns = self.cumulative_returns.squeeze(1).cpu().data.tolist()
state = self.reset() if done else self.get_state() # automatically reset in vectorized env
done = torch.tensor(done, dtype=torch.bool, device=self.device).expand(self.num_envs)
return state, reward, done, ()
def load_data_from_disk(self, tech_id_list=None):
tech_id_list = [
"macd", "boll_ub", "boll_lb", "rsi_30", "cci_30", "dx_30", "close_30_sma", "close_60_sma",
] if tech_id_list is None else tech_id_list
if os.path.exists(self.npz_pwd):
ary_dict = np.load(self.npz_pwd, allow_pickle=True)
close_ary = ary_dict['close_ary']
tech_ary = ary_dict['tech_ary']
elif os.path.exists(self.df_pwd): # convert pandas.DataFrame to numpy.array
df = pd.read_pickle(self.df_pwd)
tech_ary = []
close_ary = []
df_len = len(df.index.unique()) # df_len = max_step
for day in range(df_len):
item = df.loc[day]
tech_items = [item[tech].values.tolist() for tech in tech_id_list]
tech_items_flatten = sum(tech_items, [])
tech_ary.append(tech_items_flatten)
close_ary.append(item.close)
close_ary = np.array(close_ary)
tech_ary = np.array(tech_ary)
np.savez_compressed(self.npz_pwd, close_ary=close_ary, tech_ary=tech_ary, )
else:
error_str = f"| StockTradingEnv need {self.df_pwd} or {self.npz_pwd}" \
f"\n download the following files and save in `.`" \
f"\n https://github.com/Yonv1943/Python/blob/master/scow/China_A_shares.numpy.npz" \
f"\n https://github.com/Yonv1943/Python/blob/master/scow/China_A_shares.pandas.dataframe"
raise FileNotFoundError(error_str)
return close_ary, tech_ary