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Nguyen Viet Tuan
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import MetaTrader5 as mt5
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import pandas as pd
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import numpy as np
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import math
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import argparse
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import configparser
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from envs.real_net_env import RealNetEnv, RealNetController
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from envs.functions import getState, formatPrice
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from agents.models import MA2C
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from utils import Predictor
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SYMBOL = "EURUSD"
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DEVIATION = 20
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TIMEFRAME = mt5.TIMEFRAME_H4
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VOLUME = 0.03
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PERIOD = 11
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--config-dir', type=str, required=False,
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default='config_ma2c_real.ini', help="inference config dir")
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parser.add_argument('--port', type=int, required=False,
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default=0, help="running port")
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parser.add_argument('--policy-type', type=str, required=False, default='default',
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help="inference policy type in evaluation: default, stochastic, or deterministic")
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parser.add_argument('--position-type', type=dict, required=False,
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default={'long': 1, 'short': -1}, help="types of position")
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args = parser.parse_args()
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return args
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def market_order(symbol, volume, order_type):
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tick = mt5.symbol_info_tick(symbol)
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order_dict = {'long': 0, 'short': 1}
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price_dict = {'long': tick.ask, 'short': tick.bid}
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request = {
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"action": mt5.TRADE_ACTION_DEAL,
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"symbol": symbol,
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"volume": volume,
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"type": order_dict[order_type],
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"price": price_dict[order_type],
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"deviation": DEVIATION,
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"magic": 100,
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"comment": "python market order",
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"type_time": mt5.ORDER_TIME_GTC,
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"type_filling": mt5.ORDER_FILLING_IOC,
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}
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order_result = mt5.order_send(request)
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print(order_result)
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return order_result
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# function to close an order base don ticket id
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def close_order(ticket):
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positions = mt5.positions_get()
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for pos in positions:
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tick = mt5.symbol_info_tick(pos.symbol)
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# 0 represents buy, 1 represents sell - inverting order_type to close the position
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type_dict = {0: 1, 1: 0}
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price_dict = {0: tick.ask, 1: tick.bid}
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if pos.ticket == ticket:
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request = {
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"action": mt5.TRADE_ACTION_DEAL,
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"position": pos.ticket,
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"symbol": pos.symbol,
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"volume": pos.volume,
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"type": type_dict[pos.type],
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"price": price_dict[pos.type],
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"deviation": DEVIATION,
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"magic": 100,
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"comment": "python close order",
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"type_time": mt5.ORDER_TIME_GTC,
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"type_filling": mt5.ORDER_FILLING_IOC,
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}
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order_result = mt5.order_send(request)
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print(order_result)
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return order_result
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return 'Ticket does not exist'
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def sigmoid(x):
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return 1 / (1 + math.exp(-x))
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def _norm_clip_state(x, norm, clip=-1):
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x = x / norm
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return x if clip < 0 else np.clip(x, 0, clip)
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def getState(symbol, timeframe, period, index=1000):
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bars = mt5.copy_rates_from_pos(symbol, timeframe, 1, period)
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bars_df = pd.DataFrame(bars)
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vec = bars_df.close.tolist()
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vec = [x*index for x in vec]
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res = []
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for i in range(period - 1):
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res.append(sigmoid(vec[i + 1] - vec[i]))
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return np.array(res)
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def init_env(config, port=1, naive_policy=False):
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if not naive_policy:
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return RealNetEnv(config, port=port)
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else:
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env = RealNetEnv(config, port=port)
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policy = RealNetController(env.node_names, env.nodes)
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return env, policy
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def data_preprocessing(cur_state, norm, clip, agents):
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# hard code the state ordering as wave, wait, fp
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state = []
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# measure the most recent state
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norm_cur_state = _norm_clip_state(cur_state, norm, clip)
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# get the appropriate state vectors
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for _ in agents:
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# wave is required in state
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cur_state = [norm_cur_state]
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state.append(np.concatenate(cur_state))
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return state
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def main(args):
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config_dir = args.config_dir
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port = args.port
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policy_type = args.policy_type
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agent_type = args.position_type
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# initialize start value
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inventory = {}
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open_ticket = {}
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for agent in [*agent_type]:
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inventory[agent] = []
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open_ticket[agent] = []
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total_profit = 0
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balance_list = []
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pre_state = np.array([])
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# load config file for env
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config = configparser.ConfigParser()
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config.read(config_dir)
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cur_balance = config['ENV_CONFIG'].getint('balance')
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norm = config['ENV_CONFIG'].getfloat('norm_wave')
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clip = config['ENV_CONFIG'].getfloat('clip_wave')
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# init env
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env = init_env(config['ENV_CONFIG'], port)
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# load model for agent
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# init centralized or multi agent
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model = MA2C(env.n_s_ls, env.n_a_ls, env.n_w_ls,
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env.n_f_ls, 0, config['MODEL_CONFIG'])
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model.load('weights/')
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model.reset()
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# collect evaluation data
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predictor = Predictor(env, model, policy_type=policy_type)
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# init mt5
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mt5.initialize()
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while True:
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cur_state = getState(symbol=SYMBOL, timeframe=TIMEFRAME,
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period=PERIOD)
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if not np.array_equal(cur_state, pre_state):
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state = data_preprocessing(cur_state, norm, clip, [*agent_type])
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action = predictor.run(state)
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print('---ACTION--- :', action)
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tick = mt5.symbol_info_tick(SYMBOL)
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price_dict = {'long': tick.ask, 'short': tick.bid}
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for agent, a in zip([*agent_type], list(action)):
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if a == 1:
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market_order(SYMBOL, VOLUME, agent)
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inventory[agent].append(price_dict[agent])
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open_ticket[agent].append(mt5.positions_get()[-1].ticket)
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elif a == 2 and len(inventory[agent]) > 0:
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close_order(open_ticket[agent].pop(0))
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order_price = inventory[agent].pop(0)
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profit = (price_dict[agent] - order_price) * \
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agent_type[agent] * VOLUME * 100000
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total_profit += profit
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cur_balance += profit
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balance_list.append(round(cur_balance, 2))
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print("--------------------------------")
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print("Total Profit: " + formatPrice(total_profit))
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print('Curent Balance: ' + formatPrice(cur_balance))
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print('Balance List', balance_list)
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print("--------------------------------")
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pre_state = cur_state
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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