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agent.py
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import tensorflow as tf
import numpy as np
import random
import matplotlib.pyplot as plt
import scipy.misc
import os
from environment import Environment
class Agent():
# 代理状态配置的值数
STATE_DIM = 2 # 持股比例，投资组合价值比例
ACTION_BUY = 0
ACTION_SELL = 1
ACTION_HOLD = 2
ACTIONS = [ACTION_BUY, ACTION_SELL, ACTION_HOLD]
NUM_ACTIONS = len(ACTIONS)
TRADING_CHARGE = 0 # 0.00015
TRADING_TAX = 0 # 0.003
def __init__(self, environment, min_trading_unit=1, max_trading_unit=2, delayed_reward_threshold=.05):
self.environment = environment
# 最小交易单位、最大交易单位、延迟补偿阈值
self.min_trading_unit = min_trading_unit # 最小单交易单位
self.max_trading_unit = max_trading_unit
self.delayed_reward_threshold = delayed_reward_threshold # 延迟补偿阈值
# Agent 类的属性
self.initial_balance = 1000000 # 初期资本金
self.balance = 0 # 当前现金余额
self.num_stocks = 0 # 持有股份数
self.portfolio_value = 0 # balance + num_stocks * {当前股票价格}
self.base_portfolio_value = 0 # 前一个
self.num_buy = 0 # 收购次数
self.num_sell = 0
self.num_hold = 0
self.immediate_reward = 0 # 即时奖励
self.profit_rate_account = [] # 账号盈利
# Agent 类的状态
self.ratio_hold = 0 # 持股比例
self.ratio_portfolio_value = 0 # 投资组合价值百分比
def reset(self):
self.maximum_portfolio_value = self.initial_balance
self.minimum_portfolio_value = self.initial_balance
self.balance = self.initial_balance
self.num_stocks = 0
self.portfolio_value = self.initial_balance
self.base_portfolio_value = self.initial_balance
self.num_buy = 0
self.num_sell = 0
self.num_hold = 0
self.immediate_reward = 0
self.ratio_hold = 0
self.ratio_portfolio_value = 0
def set_balance(self, balance):
self.initial_balance = balance
def get_states(self):
self.ratio_hold = self.num_stocks / int(
self.portfolio_value / self.environment.get_price())
self.ratio_portfolio_value = self.portfolio_value / self.base_portfolio_value #每日资产净值
return [
self.ratio_hold,
self.ratio_portfolio_value
]
def decide_action(self, policy_network, sample, epsilon):
confidence = 0.
# 探险决定
if np.random.rand() < epsilon:
exploration = True
action = np.random.randint(self.NUM_ACTIONS) # 随机决定行动
else:
exploration = False
probs = policy_network.predict(sample) # 每个行动的概率
action = np.argmax(probs)
confidence = probs[action]
return action, confidence, exploration
def validate_action(self, action):
validity = True
if action == Agent.ACTION_BUY:
if self.balance < self.environment.get_price() * (
1 + self.TRADING_CHARGE) * self.min_trading_unit:
validity = False
elif action == Agent.ACTION_SELL:
if self.num_stocks <= 0:
validity = False
return validity
# confidence : 选定行为的概率值
def decide_trading_unit(self, confidence):
if np.isnan(confidence):
return self.min_trading_unit
added_traiding = max(min(
int(confidence * (self.max_trading_unit - self.min_trading_unit)),
self.max_trading_unit - self.min_trading_unit
), 0)
#added_traiding=100
return self.min_trading_unit + added_traiding
def act(self, action, confidence):
if not self.validate_action(action):
action = Agent.ACTION_HOLD
# 获取环境中的当前价格
curr_price = self.environment.get_price() ##股票价格
# 立即重置奖励
self.immediate_reward = 0
# 买入
if action == Agent.ACTION_BUY:
# 确定要购买的单位
trading_unit = self.decide_trading_unit(confidence)
balance = self.balance - curr_price * (1 + self.TRADING_CHARGE) * trading_unit
# 如果持有的现金不足，尽可能用持有的现金购买
if balance < 0:
trading_unit = max(min(
int(self.balance / (
curr_price * (1 + self.TRADING_CHARGE))), self.max_trading_unit),
self.min_trading_unit
)
# 应用手续费计算总买入金额
invest_amount = curr_price * (1 + self.TRADING_CHARGE) * trading_unit
self.balance -= invest_amount # 刷新持有现金
self.num_stocks += trading_unit
self.num_buy += 1 # 매수 횟수 증가
#self.states_buy.append(self.environment.idx)
# 卖出
elif action == Agent.ACTION_SELL:
# 确定要出售的单位
trading_unit = self.decide_trading_unit(confidence)
# 如果持有的股票不足，尽可能最大限度地抛售
trading_unit = min(trading_unit, self.num_stocks)
# 抛售
invest_amount = curr_price * (
1 - (self.TRADING_TAX + self.TRADING_CHARGE)) * trading_unit
self.num_stocks -= trading_unit
self.balance += invest_amount
self.num_sell += 1
#self.states_sell.append(self.environment.idx)
# 保持
elif action == Agent.ACTION_HOLD:
self.num_hold += 1 # 持球次数增加
# 产品组合价值更新
self.portfolio_value = self.balance + curr_price * self.num_stocks #组合价值，收益，用这个计算日回报率
if self.maximum_portfolio_value < self.portfolio_value:
self.maximum_portfolio_value = self.portfolio_value
elif self.minimum_portfolio_value > self.portfolio_value:
self.minimum_portfolio_value = self.portfolio_value
profitloss = (
(self.portfolio_value - self.base_portfolio_value) / self.base_portfolio_value)*100
#self.profit_rate_account.append(profitloss)
#利润增长率 今日净值增长率=（今日净值－昨日净值）/昨日净值
#print(profitloss)
#self.base_portfolio_value = self.portfolio_value
return profitloss#self.immediate_reward
#return profitloss #self.immediate_reward, delayed_reward