[
{
"path": ".gitignore",
"content": ".idea/\n\n# Python\ntrain/__pycache__/\n*.pyc\n\n# data\n/data/\n\nconfig.py\n"
},
{
"path": "LICENSE",
"content": "Copyright (C) 2017 Hung I, Chiu\n\n Forex-DQN is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n Forex-DQN is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. \n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n Preamble\n\n The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works. By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users. We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors. 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Interpretation of Sections 15 and 16.\n\n If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n END OF TERMS AND CONDITIONS\n\n How to Apply These Terms to Your New Programs\n\n If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n To do so, attach the following notices to the program. It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n \n Copyright (C) \n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n\nAlso add information on how to contact you by electronic and paper mail.\n\n If the program does terminal interaction, make it output a short\nnotice like this when it starts in an interactive mode:\n\n Copyright (C) \n This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.\n This is free software, and you are welcome to redistribute it\n under certain conditions; type `show c' for details.\n\nThe hypothetical commands `show w' and `show c' should show the appropriate\nparts of the General Public License. Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n.\n\n The GNU General Public License does not permit incorporating your program\ninto proprietary programs. If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library. If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License. But first, please read\n."
},
{
"path": "README.md",
"content": "# Forex-DQN\n\n## NOT MAINTAINED\n\nThis project is build to trade forex automatically. It is build upon the concept of DQN (Deep Q Network) and projects below.\n\n- [MorvanZhou/Tutorials](https://github.com/MorvanZhou/tutorials)\n- [devsisters/DQN-tensorflow](https://github.com/devsisters/DQN-tensorflow)\n\n## Features\n- Support tensorboard\n\n\n\n\n\n## Installation\n\n```\npip3 install -r requirements.txt\n```\n\n## Config (Required)\n\nYou must create your own config ```train/config.py```. To check the format of the config, you can take a look at ```train/config-tmpl.py```\n\n## Script\n\n#### Train\n\n```\npython3 train/index.py\n```\n\n#### Help\n\n```\npython3 train/index.py -h\n```\n\n## LICENSE\n\n#### GPL-3.0\n"
},
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{
"path": "requirements.txt",
"content": "appdirs==1.4.2\ncycler==0.10.0\nimageio==2.1.2\nmujoco-py==0.5.7\nnumpy==1.12.0\nolefile==0.44\npachi-py==0.0.21\npackaging==16.8\npandas==0.19.2\nPillow==4.0.0\nprotobuf==3.2.0\npyglet==1.2.4\nPyOpenGL==3.1.0\npyparsing==2.1.10\npython-dateutil==2.6.0\npytz==2016.10\nPyYAML==3.12\nrequests==2.13.0\nscikit-learn==0.18.1\nscipy==0.19.0\nsix==1.10.0\nsklearn==0.0\ntensorflow==1.0.0\ntermcolor==1.1.0\ntqdm==4.11.2\ntzlocal==1.3\n"
},
{
"path": "train/RLBrain.py",
"content": "import numpy as np\nimport tensorflow as tf\nimport datetime as dt\n\nfrom account import Account\nfrom constants import STOP, BUY, SELL, CLOSE, SHOW_HAND\nfrom helper import mkdir\n\nnp.random.seed(dt.datetime.now().microsecond)\ntf.set_random_seed(dt.datetime.now().microsecond)\n\nclass DeepNetwork:\n def __init__(\n self,\n forex,\n dates,\n featureNum,\n config,\n ):\n self.forex = forex\n self.dates = dates\n self.config = config\n\n self.episodes = config['episodes']\n self.interval = config['interval']\n\n self.n_actions = 4\n self.n_features = featureNum * config['count']\n self.lr = config['learning_rate']\n self.gamma = config['reward_decay']\n self.epsilon_max = config['e_greedy'] if config['isTrain'] else 1\n self.replace_target_iter = config['replace_target_iter']\n self.memory_size = config['memory_size']\n self.batch_size = config['batch_size']\n self.epsilon_increment = config['e_greedy_increment']\n self.epsilon = 0 if config['e_greedy_increment'] is not None else self.epsilon_max\n\n # account\n self.initBalance = 100000\n\n self.isTrain = config['isTrain']\n self.dir = config['dir']\n\n self.ckptFile = config['ckptFile']\n self.ckptSavePeriod = config['ckptSavePeriod']\n\n # total learning step\n self.step = -config['startStep']\n self.totalLoss = 0\n self.totalMaxQ = 0\n self.r_actions = []\n\n # initialize zero memory [s, a, r, s_]\n self.memory = np.zeros((self.memory_size, self.n_features * 2 + 2))\n self.memory_counter = 0\n\n self.sess = tf.Session()\n\n # consist of [target_net, evaluate_net]\n self.buildNet()\n\n self.saver = tf.train.Saver(max_to_keep = int(self.episodes / self.ckptSavePeriod))\n\n if config['isLoad'] or not self.isTrain:\n self.saver.restore(self.sess, 'data/%s/%s' % (self.dir, self.ckptFile))\n print('Load data/%s/%s sucessfully!\\n' % (self.dir, self.ckptFile))\n else:\n self.sess.run(tf.global_variables_initializer())\n print('Apply global initializer!\\n')\n\n def subTrain(self, isTrain, dates):\n account = Account(\n balance = self.initBalance,\n cliOutput = self.config['cliOutput'],\n )\n for date in dates:\n self.forex.setDate(date)\n\n startTime, endTime = self.forex.getTime()\n price, state = self.forex.getPrice(startTime)\n\n for time in range(startTime, endTime - self.interval, self.interval):\n # Q learning start\n action = self.chooseAction(state, isTrain)\n\n reward = 0\n if action == STOP:\n reward = account.stop()\n elif action == BUY or action == SELL:\n reward = account.order(\n price, # price is at column 0\n {\n 'type': action,\n 'unit': SHOW_HAND,\n },\n time = time\n )\n elif action == CLOSE:\n reward = account.closePosition(price, time = time)\n\n price, state_ = self.forex.getPrice(time + self.interval)\n\n self.storeTransition(\n transition = np.hstack((state, [action, reward], state_)),\n mode = 0 if isTrain else 1,\n )\n\n if isTrain:\n if self.step > 0 and self.step % self.config['learn_period'] == 0:\n self.learn()\n self.step += 1\n\n state = state_\n\n return account.balance\n\n def train(self):\n if self.isTrain:\n print('Start training\\n')\n else:\n print('Start testing\\n')\n\n for episode in range(self.episodes):\n if episode % 10 == 0:\n print('episode', episode)\n\n if self.isTrain:\n epsilonBalance = self.subTrain(isTrain = True, dates = self.dates)\n\n # to get the actual balance\n realBalance = self.subTrain(isTrain = False, dates = self.dates)\n\n self.finishEpisode(episode, epsilonBalance, realBalance)\n else:\n if self.config['cliOutput']:\n print(Account.getCloseHeader())\n\n print(self.subTrain(isTrain = False, dates = self.dates))\n\n if self.isTrain:\n print('Finish training\\n')\n else:\n print('Finish testing\\n')\n\n def optimize(self):\n if self.config['optimizer'] == 'RMSProp':\n return tf.train.RMSPropOptimizer(\n self.lr,\n decay = 0.9 if self.config['op_decay'] == None else self.config['op_decay'],\n momentum = 0.0 if self.config['op_momentum'] == None else self.config['op_momentum'],\n epsilon = pow(10, -10) if self.config['op_epsilon'] == None else self.config['op_epsilon'],\n ).minimize(self.loss)\n\n def buildNet(self):\n def addLayer(\n name,\n input,\n output_dim,\n w_init,\n b_init,\n c_names,\n active_fn = None,\n ):\n with tf.variable_scope(name):\n w = tf.get_variable('w', [input.get_shape().as_list()[1], output_dim],\n initializer = w_init, collections = c_names)\n b = tf.get_variable('b', [1, output_dim], initializer = b_init, collections = c_names)\n\n if active_fn != None:\n out = active_fn(tf.matmul(input, w) + b)\n else:\n out = tf.matmul(input, w) + b\n\n return out, w, b\n\n # ------------------ build evaluate_net ------------------\n self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s') # input\n self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target') # for calculating loss\n\n # config of layers\n self.w = {}\n\n self.w_init = \\\n tf.random_normal_initializer(self.config['init_w_mean'], self.config['init_w_std'])\n self.b_init = \\\n tf.constant_initializer(self.config['init_b'])\n\n active_fn = tf.nn.relu\n\n with tf.variable_scope('eval_net'):\n # c_names(collections_names) are the collections to store variables\n c_names = ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES]\n\n self.w['l1_o'], self.w['l1_w'], self.w['l1_b'] =\\\n addLayer('l1', self.s, self.config['l1_dim'], self.w_init, self.b_init, c_names, active_fn)\n\n # output layer\n self.q_eval, self.w['lout_w'], self.w['lout_b'] = \\\n addLayer('lout', self.w['l1_o'], self.n_actions, self.w_init, self.b_init, c_names)\n\n with tf.name_scope('loss'):\n self.loss = tf.reduce_sum(tf.squared_difference(self.q_target, self.q_eval))\n\n with tf.name_scope('train'):\n self._train_op = self.optimize()\n\n # ------------------ build target_net ------------------\n self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input\n self.t_w = {}\n\n with tf.variable_scope('target_net'):\n c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]\n\n self.t_w['l1_o'], self.t_w['l1_w'], self.t_w['l1_b'] = \\\n addLayer('l1', self.s_, self.config['l1_dim'], self.w_init, self.b_init, c_names, active_fn)\n\n # output layer\n self.q_next, self.t_w['lout_w'], self.t_w['lout_b'] = \\\n addLayer('lout', self.t_w['l1_o'], self.n_actions, self.w_init, self.b_init, c_names)\n\n with tf.variable_scope('summary'):\n # e_XX means with epsilon\n # r_XX means without epsilon, which is real simulation\n scalar_summary_tags = ['loss_avg', 'e_balance', 'r_balance',\n 'q_max', 'q_total', 'epsilon']\n\n self.summary_placeholders = {}\n self.summary_ops = {}\n\n for tag in scalar_summary_tags:\n self.summary_placeholders[tag] = tf.placeholder('float32', None, name=tag.replace(' ', '_') + '_0')\n self.summary_ops[tag] = tf.summary.scalar(tag, self.summary_placeholders[tag])\n\n histogram_summary_tags = ['r_actions']\n\n for tag in histogram_summary_tags:\n self.summary_placeholders[tag] = tf.placeholder('float32', None, name=tag.replace(' ', '_') + '_0')\n self.summary_ops[tag] = tf.summary.histogram(tag, self.summary_placeholders[tag])\n\n with tf.variable_scope('param'):\n w_c_names = 'eval_net_params_summaries'\n histogram_w_tags = ['l1_w', 'l1_b', 'lout_w', 'lout_b']\n for tag in histogram_w_tags:\n tf.summary.histogram(tag, self.w[tag], collections = [w_c_names])\n\n if self.isTrain:\n self.merged = tf.summary.merge_all(key = w_c_names)\n self.writer = tf.summary.FileWriter('data/' + self.dir, self.sess.graph)\n\n # state action reward next state\n # mode: 0: store train 1: store test\n def storeTransition(self, transition, mode = 0):\n # replace the old memory with new memory\n self.memory[self.memory_counter % self.memory_size] = transition\n self.memory_counter += 1\n\n def chooseAction(self, observation, isTrain = True):\n # to have batch dimension when feed into tf placeholder\n observation = observation[np.newaxis, :]\n\n if not isTrain:\n actions = self.sess.run(self.q_eval, feed_dict={self.s: observation})\n action = np.argmax(actions)\n self.r_actions.append(action)\n\n elif self.step < 0 or np.random.uniform() >= self.epsilon:\n action = np.random.randint(0, self.n_actions)\n\n else:\n # forward feed the observation and get q value for every actions\n actions = self.sess.run(self.q_eval, feed_dict={self.s: observation})\n action = np.argmax(actions)\n\n return action\n\n def replaceTargetParams(self):\n t_params = tf.get_collection('target_net_params')\n e_params = tf.get_collection('eval_net_params')\n self.sess.run([tf.assign(t, e) for t, e in zip(t_params, e_params)])\n\n def learn(self):\n # check to replace target parameters\n if self.step % self.replace_target_iter == 0:\n self.replaceTargetParams()\n\n # sample batch memory from all memory\n batch_memory =\\\n self.memory[np.random.choice(\n self.memory_size\\\n if self.memory_counter > self.memory_size\\\n else self.memory_counter,\n self.batch_size), :]\n\n q_next, q_eval = self.sess.run(\n [self.q_next, self.q_eval],\n feed_dict={\n self.s_: batch_memory[:, -self.n_features:],\n self.s: batch_memory[:, :self.n_features]\n })\n\n # change q_target w.r.t q_eval's action\n q_target = q_eval.copy()\n\n q_target[np.arange(self.batch_size), batch_memory[:, self.n_features].astype(int)] = \\\n batch_memory[:, self.n_features + 1] + self.gamma * np.max(q_next, axis=1)\n\n # train eval network\n _, self.param_summary, cost = \\\n self.sess.run([self._train_op, self.merged, self.loss],\n feed_dict={self.s: batch_memory[:, :self.n_features],\n self.q_target: q_target,\n })\n\n # increasing epsilon\n self.epsilon =\\\n self.epsilon + self.epsilon_increment\\\n if self.epsilon < self.epsilon_max\\\n else self.epsilon_max\n self.totalLoss += cost\n self.totalQ += q_eval.mean(axis = 1).mean(axis = 0)\n self.totalMaxQ += np.max(q_eval, axis=1).mean()\n\n # mode 0: normal save, 1: period save\n def saveParam(self, dir = 'tmp', mode = 0):\n subdir = ''\n\n if mode == 1:\n subdir = 'history/%s/' % (dir)\n\n fulldir = 'data/%s/%s' % (self.dir, subdir)\n\n mkdir(fulldir)\n self.saver.save(self.sess, '%s%s' % (fulldir, self.ckptFile))\n\n def injectSummary(self, tag_dict, episode):\n summary_str_lists = self.sess.run([self.summary_ops[tag] for tag in tag_dict.keys()], {\n self.summary_placeholders[tag]: value for tag, value in tag_dict.items()\n })\n for summary_str in summary_str_lists:\n self.writer.add_summary(summary_str, episode)\n\n self.writer.add_summary(self.param_summary, episode)\n\n def finishEpisode(self, episode, epsilonBalance, realBalance):\n if self.step > 0:\n injectDict = {\n # scalar\n 'loss_avg': self.totalLoss,\n 'e_balance': epsilonBalance,\n 'r_balance': realBalance,\n 'epsilon': self.epsilon,\n 'q_max': self.totalMaxQ,\n 'q_total': self.totalQ,\n # histogram\n 'r_actions': self.r_actions,\n }\n self.injectSummary(injectDict, episode)\n\n self.saveParam(mode = 0)\n if episode % self.ckptSavePeriod == 0:\n self.saveParam(dir = '%d' % (episode), mode = 1)\n\n self.r_actions = []\n self.totalLoss = 0\n self.totalQ = 0\n self.totalMaxQ = 0\n"
},
{
"path": "train/account.py",
"content": "from helper import getUTCTimeStrFromMs\nfrom constants import BUY, SELL, SHOW_HAND\n\nclass Account:\n def __init__(\n self,\n leverage = 50,\n balance = 100000,\n cliOutput = False,\n ):\n self.leverage = leverage\n self.balance = balance\n self.position = 0\n self.price = 0\n self.units = 0\n self.showHand = False # False means hasn't show hand, True means show hand\n self.openTime = 0\n\n self.cliOutput = cliOutput\n\n self.itemHeader = [\n 'type',\n 'price',\n 'closePrice',\n 'units',\n 'pl',\n 'balance',\n 'openTime',\n 'closeTime',\n ]\n\n def order(\n self,\n price,\n action,\n time,\n ):\n if (action['type'] != BUY and action['type'] != SELL) \\\n or self.balance < 100 \\\n or self.showHand: # to discourage repeat order\n return 0\n\n posNeg = 1 if action['type'] == BUY else -1\n\n if action['unit'] == SHOW_HAND:\n unit = int(self.balance * self.leverage / price)\n self.showHand = True\n else:\n unit = action['unit']\n\n self.position += unit * price * posNeg\n self.units += unit * posNeg\n\n self.price = self.position / self.units\n if self.openTime == 0:\n self.openTime = time\n\n return 0\n\n def closePosition(\n self,\n price,\n time,\n ):\n if self.units == 0:\n return 0\n\n pl = \\\n (price - self.price) * self.units \\\n if self.units > 0 \\\n else (self.price - price) * -self.units\n\n balance = self.balance\n\n if self.cliOutput:\n print([\n 'B' if self.units > 0 else 'S',\n '%.5f' % self.price,\n '%.5f' % price,\n abs(self.units),\n '%.2f' % pl,\n '%.2f' % (balance + pl),\n getUTCTimeStrFromMs(self.openTime),\n getUTCTimeStrFromMs(time),\n ])\n\n self.balance += pl\n self.pl = 0\n self.position = 0\n self.price = 0\n self.units = 0\n self.showHand = False\n self.openTime = 0\n\n return pl * 100 / balance\n\n def stop(self):\n return 0\n\n @staticmethod\n def getCloseHeader():\n return Account().itemHeader\n"
},
{
"path": "train/config-tmpl.py",
"content": "def getConfig():\n return {\n 'count': 3,\n 'dataType': 'mid',\n\n # RL\n 'episodes': 10000000,\n 'interval': 5 * 60 * 1000,\n\n 'learning_rate': 0.9,\n\n 'reward_decay': 0.9,\n 'e_greedy': 1.,\n 'replace_target_iter': 30000,\n 'memory_size': 1500000,\n 'batch_size': 32,\n 'e_greedy_increment': 0.000001,\n 'startStep': 1500,\n\n 'learn_period': 1,\n\n # network\n 'l1_dim': 60,\n\n 'init_w_mean': 0.,\n 'init_w_std': 0.01,\n 'init_b': 0.01,\n\n 'isTrain': True,\n 'isLoad': False, # for continuing previous training\n\n 'optimizer': 'RMSProp',\n 'op_rho': None,\n 'op_epsilon': None,\n 'op_decay': None,\n 'op_momentum': 0.95,\n\n # date\n 'dateStart': '20170101',\n 'dateEnd': '20170101',\n\n # output\n 'ckptFile': 'train.ckpt',\n 'ckptSavePeriod': 10,\n 'cliOutput': False,\n 'loadHisNum': 0,\n\n # dir\n 'dir': 'tmp',\n\n 'rmHeader': ['rmHeader',\n 'episodes',\n 'isTrain', 'isLoad',\n 'ckptFile', 'ckptSavePeriod',\n 'cliOutput', 'noLoadHeader',\n 'loadHisNum'],\n\n 'noLoadHeader': ['episodes', 'dateStart', 'dateEnd'],\n }\n"
},
{
"path": "train/constants.py",
"content": "# action\nBUY = 0\nSELL = 1\nSTOP = 2\nCLOSE = 3\n\n# unit\nSHOW_HAND = -1\n"
},
{
"path": "train/dataPicker.py",
"content": "from helper import getMsByTimeStr\nimport numpy as np\n\ndef dataContentPicker(\n data,\n dataType,\n):\n if dataType == 'mid':\n if data['complete']:\n return [\n data['mid']['o'],\n data['mid']['h'],\n data['mid']['l'],\n data['mid']['c'],\n ]\n else:\n return [0, 0, 0, 0]\n\ndef dataPicker(\n data,\n dataType = 'mid',\n):\n arr = dataContentPicker(data, dataType)\n return np.concatenate((\n arr if type(arr).__module__ == 'numpy' else np.array(arr),\n [1 if data['complete'] else 0, getMsByTimeStr(data['time'])]\n ),axis = 0)\n"
},
{
"path": "train/forex.py",
"content": "import numpy as np\n\nclass Forex:\n def __init__(\n self,\n midPriceMap,\n count,\n ):\n self.midPriceMap = midPriceMap\n self.midPriceArr = None\n self.midPricePtr = 0\n\n self.date = ''\n self.count = count\n\n self.featureLen = 0\n\n def setDate(self, date):\n self.date = date\n\n self.midPriceArr = self.midPriceMap[date]\n self.midPricePtr = len(self.midPriceArr) - 1\n self.featureLen = self.count * (len(self.midPriceArr[0]) - 2)\n\n def getTime(self):\n # number time is at last column\n startTime = int(self.midPriceArr[-1, -1]) + 5 * 60 * 1000 * self.count\n endTime = int(startTime + 5 * 86400 * 1000) # number\n return startTime, endTime\n\n def getPrice(self, time):\n while self.midPricePtr >= 0 and time >= self.midPriceArr[self.midPricePtr, -1]:\n self.midPricePtr -= 1\n\n self.midPricePtr += 1\n\n observation = \\\n np.reshape(self.midPriceArr[self.midPricePtr + 1 : self.midPricePtr + self.count + 1, 0:-2],\n self.featureLen)\n\n price = self.midPriceArr[self.midPricePtr, 0]\n\n return price, observation\n"
},
{
"path": "train/helper.py",
"content": "import datetime as dt\nimport pandas as pd\nimport os\n\nepoch = dt.datetime.utcfromtimestamp(0)\n\ndef getMsByTime(dt):\n return int((dt - epoch).total_seconds() * 1000)\n\ndef getUTCTimeStrFromMs(time):\n return dt.datetime.fromtimestamp((time - 8 * 60 * 60 * 1000) / 1000.0)\n\ndef getTime(timeStr):\n return dt.datetime.strptime(timeStr,'%Y-%m-%dT%H:%M:%S.000000000Z')\n\ndef getMsByTimeStr(timeStr):\n return getMsByTime(getTime(timeStr))\n\ndef getDatesArr(startDate, endDate, str = '%Y-%m-%d'):\n return [d.strftime(str) for d in pd.date_range(startDate, endDate, freq='7D')]\n\ndef mkdir(dir):\n if not os.path.exists(dir):\n os.makedirs(dir)\n"
},
{
"path": "train/index.py",
"content": "import numpy as np\nimport json\nimport csv\nimport os\nimport sys\nimport getopt\nimport tensorflow as tf\n\nfrom forex import Forex\nfrom RLBrain import DeepNetwork\nfrom config import getConfig\nfrom dataPicker import dataPicker\nfrom helper import getDatesArr, mkdir\n\ndef readData(\n dates,\n dataType = 'mid',\n):\n\n midPriceMap = {}\n featureNum = 0\n\n # start read\n for date in dates:\n tmp = []\n with open('%s/%s.json' % ('candles', date)) as file:\n json_data = json.load(file)\n featureNum = len(dataPicker(json_data['candles'][0], dataType = dataType)) - 2\n\n for data in json_data['candles']:\n tmp.append(dataPicker(data, dataType = dataType))\n \n # print(tmp)\n midPriceMap[date] = np.array(tmp)\n\n print('Read file done')\n\n return midPriceMap, featureNum\n\nif __name__ == '__main__':\n\n FLAG = getConfig()\n\n try:\n opts, args = getopt.getopt(\n sys.argv[1:],\n 'cd:hl:ot',\n ['dir=', 'help'],\n )\n except getopt.GetoptError:\n print('python3 train/index.py -h')\n sys.exit(2)\n for opt, arg in opts:\n if opt in ('-c'):\n FLAG['isTrain'] = False\n FLAG['episodes'] = 1\n FLAG['outputClose'] = True\n elif opt in ('-d', '--dir'):\n FLAG['dir'] = arg\n elif opt in ['-h', '--help']:\n print('python3 train/index.py')\n print('-d : assign the output directory to data/')\n print('-h --help: help')\n print('-l : load ckpt file from /history/')\n print('-o: command line output result')\n print('-t: testing mode, default: training mode')\n sys.exit()\n elif opt in ('-l'):\n FLAG['ckptFile'] = 'history/%s/train.ckpt' % (arg)\n FLAG['loadHisNum'] = int(arg)\n elif opt in ('-o'):\n FLAG['isTrain'] = False\n FLAG['episodes'] = 1\n FLAG['cliOutput'] = True\n elif opt in ('-t'):\n FLAG['isTrain'] = False\n FLAG['episodes'] = 1\n\n mkdir('data')\n mkdir('data/%s' % (FLAG['dir']))\n mkdir('data/%s/history' % (FLAG['dir']))\n\n if not FLAG['isTrain']:\n with open('data/%s/param.csv' % (FLAG['dir'])) as csv_file:\n r = csv.DictReader(csv_file)\n for row in r:\n for column, value in row.items():\n if value != None and not column in FLAG['noLoadHeader']:\n if isinstance(FLAG[column], int):\n FLAG[column] = int(value)\n elif isinstance(FLAG[column], float):\n FLAG[column] = float(value)\n else:\n FLAG[column] = value\n FLAG['episodes'] = 1\n\n attr = sorted([a for a in FLAG.items()\n if not a[0] in FLAG['rmHeader']])\n\n print('')\n for key, value in attr:\n print('%s: %s' % (key, value))\n print('')\n\n if FLAG['isTrain']:\n item = [value for key, value in attr]\n itemHeader = [key for key, value in attr]\n\n if not os.path.isfile('data/%s/param.csv' % (FLAG['dir'])):\n with open('data/%s/param.csv' % (FLAG['dir']), 'a') as csv_file:\n w = csv.writer(csv_file)\n w.writerow(itemHeader)\n w.writerow(item)\n\n dates = getDatesArr(FLAG['dateStart'], FLAG['dateEnd'])\n\n midPriceMap, featureNum = \\\n readData(\n dates = dates,\n dataType = FLAG['dataType'],\n )\n\n forex = Forex(\n midPriceMap = midPriceMap,\n count = FLAG['count'],\n )\n\n RL = DeepNetwork(\n forex = forex,\n dates = dates,\n featureNum = featureNum,\n config = FLAG,\n )\n\n RL.train()\n"
}
]