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| import os, gym import datetime import gym_anytrading import matplotlib.pyplot as plt from gym_anytrading.envs import TradingEnv, ForexEnv, StocksEnv, Actions, Positions from gym_anytrading.datasets import FOREX_EURUSD_1H_ASK, STOCKS_GOOGL from stable_baselines.common.vec_env import DummyVecEnv from stable_baselines import PPO2 from stable_baselines import ACKTR from stable_baselines.bench import Monitor from stable_baselines.common import set_global_seeds
import numpy as np import matplotlib.pyplot as plt
def simulation(i, window_size): global means # ログフォルダの生成 log_dir = './logs/' os.makedirs(log_dir, exist_ok=True)
# [20] 2020-06-28 10:00 idx1 = 50 # idx2 = 350 # データ数 span = idx2 - idx1
# 環境の生成 env = gym.make('forex-v0', frame_bound=(idx1, idx2), window_size=window_size) env = Monitor(env, log_dir, allow_early_resets=True)
# シードの指定 env.seed(0) set_global_seeds(0)
# ベクトル化環境の生成 env = DummyVecEnv([lambda: env])
# モデルの生成 #model = PPO2('MlpPolicy', env, verbose=1) model = ACKTR('MlpPolicy', env, verbose=1)
# モデルの読み込み #model = PPO2.load('trading_model{}'.format(i)) model = ACKTR.load('trading_model{}'.format(i))
# モデルの学習 #model.learn(total_timesteps=128000)
# モデルの保存 #model.save('trading_model{}'.format(i))
# モデルのテスト #env = gym.make('forex-v0', frame_bound=(idx2+500, idx2 + span+500), window_size=20) env = gym.make('forex-v0', frame_bound=(idx1+200, idx2+200), window_size=window_size) env.seed(0) state = env.reset() while True: # 行動の取得 action, _ = model.predict(state) # 1ステップ実行 state, reward, done, info = env.step(action) # エピソード完了 if done: print('info:', info, info['total_reward']) means.append(info['total_reward']) break # グラフのプロット plt.cla() env.render_all() #plt.savefig('trading{:%Y%m%d_%H%M%S}.png'.format(datetime.datetime.now())) #plt.show()
#with open('C:/Util/anaconda3/envs/openai_gym/lib/site-packages/gym_anytrading/datasets/data/FOREX_EURUSD_1H_ASK.csv', 'r') as f: # lines = f.readlines() # s1 = lines[idx1].split(',')[0] # s2 = lines[idx2-1].split(',')[0] # #print(s1,s2, (idx2 - idx1)) # # s3 = lines[idx2].split(',')[0] # s4 = lines[idx2+span].split(',')[0] # #print(s3,s4, (idx2+span - idx2))
labels = [] means = []
for i in range(50): labels.append('{}'.format(i)) simulation(9, 50)
x = np.arange(len(labels)) width = 0.35
fig, ax = plt.subplots()
rect = ax.bar(x, means, width) ax.set_xticks(x) ax.set_xticklabels(labels)
#plt.show() plt.savefig('trading{:%Y%m%d_%H%M%S}.png'.format(datetime.datetime.now()))
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