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StockTradingVmapEnv.py
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StockTradingVmapEnv.py
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import os
import torch
import numpy as np
import numpy.random as rd
import pandas as pd
from functorch import vmap
"""finance environment
Source:
https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/Demo_China_A_share_market.ipynb
Modify: Github YonV1943
"""
'''vmap function'''
def _get_total_asset(close, shares, amount):
return (close * shares).sum() + amount # total_asset
def _get_state(amount, shares, close, tech):
return torch.cat((amount, shares, close, tech))
def _inplace_amount_shares_when_buy(amount, shares, stock_action, close, buy_cost_rate):
stock_delta = torch.min(stock_action, torch.div(amount, close, rounding_mode='floor'))
amount -= close * stock_delta * buy_cost_rate
shares += stock_delta
return torch.zeros(1)
def _inplace_amount_shares_when_sell(amount, shares, stock_action, close, sell_cost_rate):
stock_delta = torch.min(-stock_action, shares)
amount += close * stock_delta * sell_cost_rate
shares -= stock_delta
return torch.zeros(1)
class StockTradingVmapEnv:
def __init__(self, initial_amount=1e6, max_stock=100, buy_cost_pct=1e-3, sell_cost_pct=1e-3, gamma=0.99,
beg_idx=0, end_idx=1113, gpu_id: int = 0, num_envs: int = 4):
self.df_pwd = './China_A_shares.pandas.dataframe'
'''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]
print(f"| StockTradingEnv: close_ary.shape {close_ary.shape}")
print(f"| StockTradingEnv: tech_ary.shape {tech_ary.shape}")
self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
self.num_envs = num_envs
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)
'''init'''
self.gamma = gamma
self.max_stock = max_stock
self.initial_amount = initial_amount
self.max_step = self.close_price.shape[0]
self.buy_cost_rate = 1. + buy_cost_pct
self.sell_cost_rate = 1. - sell_cost_pct
'''init (set in reset)'''
self.day = None
self.rewards = None
self.total_asset = None
self.if_random_reset = True
self.cumulative_returns = None
self.amount = None
self.shares = None
self.shares_num = self.close_price.shape[1]
amount_dim = 1
'''environment information'''
self.env_name = 'StockTradingEnvVMAP-v2'
self.state_dim = self.shares_num + self.close_price.shape[1] + self.tech_factor.shape[1] + amount_dim
self.action_dim = self.shares_num
self.if_discrete = False
'''vmap function'''
self.vmap_get_total_asset = vmap(
func=_get_total_asset, in_dims=(None, 0, 0), out_dims=0)
self.vmap_get_state = vmap(
func=_get_state, in_dims=(0, 0, None, None), 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.shares_num), dtype=torch.float32, device=self.device)
if self.if_random_reset:
self.amount *= torch.rand((self.num_envs, 1), dtype=torch.float32, device=self.device) * 0.10 + 0.95
self.shares += torch.randint(0, int(self.max_stock),
size=(self.num_envs, self.shares_num), device=self.device)
self.rewards = list()
self.total_asset = self.vmap_get_total_asset(self.close_price[self.day],
self.shares,
self.amount)
state = self.get_state()
return state
def get_state(self):
return self.vmap_get_state(self.amount * 2 ** 16,
self.shares * 2 ** -9,
self.close_price[self.day] * 2 ** -7,
self.tech_factor[self.day] * 2 ** -6) # state
def step(self, action):
self.day += 1
action = action.clone()
action[(-0.1 < action) & (action < 0.1)] = 0
stock_action = (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.shares_num):
buy_idx = torch.where(stock_action[:, 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,
stock_action[buy_idx, i],
self.close_price[self.day, i],
self.buy_cost_rate)
self.amount[buy_idx] = part_amount
self.shares[buy_idx, i] = part_shares
sell_idx = torch.where((stock_action < 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,
stock_action[sell_idx, i],
self.close_price[self.day, i],
self.sell_cost_rate)
self.amount[sell_idx] = part_amount
self.shares[sell_idx, i] = part_shares
state = self.get_state()
total_asset = self.vmap_get_total_asset(self.close_price[self.day],
self.shares,
self.amount)
reward = (total_asset - self.total_asset) * 2 ** -6
self.rewards.append(reward)
self.total_asset = total_asset
done = self.day == self.max_step - 1
if done:
reward += 1. / (1. - self.gamma) * torch.stack(self.rewards).mean(dim=0)
self.cumulative_returns = total_asset / self.initial_amount
self.cumulative_returns = self.cumulative_returns.mean().item()
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.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)
else:
error_str = f"| StockTradingEnv need {self.df_pwd}" \
f"\n download the following files and save in `.`" \
f"\n https://github.com/Yonv1943/Python/blob/master/scow/China_A_shares.pandas.dataframe (2MB)"
raise FileNotFoundError(error_str)
return close_ary, tech_ary
def check_env():
gpu_id = 0
env_num = 32
env = StockTradingVmapEnv(beg_idx=834, end_idx=1113, gpu_id=gpu_id, num_envs=env_num)
env.if_random_reset = False
evaluate_time = 4
"""
env = StockTradingEnv(beg_idx=0, end_idx=1113)
cumulative_returns of random action : 1.63
cumulative_returns of buy all share : 2.80
env = StockTradingEnv(beg_idx=0, end_idx=834)
cumulative_returns of random action : 1.94
cumulative_returns of buy all share : 2.51
env = StockTradingEnv(beg_idx=834, end_idx=1113)
cumulative_returns of random action : 1.12
cumulative_returns of buy all share : 1.19
"""
print()
policy_name = 'random action'
state = env.reset()
for _ in range(env.max_step * evaluate_time):
action = torch.rand((env.num_envs, env.action_dim), dtype=torch.float32, device=env.device) * 2. - 1.
state, reward, done, _ = env.step(action)
if torch.all(done):
print(f'cumulative_returns of {policy_name}: {env.cumulative_returns:9.2f}')
state = env.reset()
dir(state)
print()
policy_name = 'buy all share (if_random_reset = False)'
env.if_random_reset = False
state = env.reset()
for _ in range(env.max_step * evaluate_time):
action = torch.ones((env.num_envs, env.action_dim), dtype=torch.float32, device=env.device) * 2. - 1.
state, reward, done, _ = env.step(action)
if torch.all(done):
print(f'cumulative_returns of {policy_name}: {env.cumulative_returns:9.2f}')
state = env.reset()
dir(state)
print()
print()
policy_name = 'buy all share (if_random_reset = True)'
env.if_random_reset = True
state = env.reset()
for _ in range(env.max_step * evaluate_time):
action = torch.ones((env.num_envs, env.action_dim), dtype=torch.float32, device=env.device) * 2. - 1.
state, reward, done, _ = env.step(action)
if torch.all(done):
print(f'cumulative_returns of {policy_name}: {env.cumulative_returns:9.2f}')
state = env.reset()
dir(state)
print()
if __name__ == '__main__':
check_env()