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main.py

@@ -3,24 +3,26 @@
 
 """
 
-from helpers.oanda_api_helpers import TradingSession, close_order_manually
+from apscheduler.schedulers.blocking import BlockingScheduler
+from helpers.oanda_api_helpers import TradingSession
 from helpers.utils import remove_nan_rows
 from helpers.get_features import get_features, min_max_scaling
 from helpers.get_historical_data import get_latest_oanda_data
 import tensorflow as tf
 import numpy as np
 import pandas as pd
 import datetime
-from apscheduler.schedulers.blocking import BlockingScheduler
+import time
+import pytz
 
-# TODO: trading params like position size kelly criterion
-position_size = 10000
+
+# some parameters
 close_current_positions = True
-start_on_spot = False
+start_on_spot = True
 
 # oanda access keys
-accountID = ''
-access_token = ''
+accountID = '101-004-3943081-006'
+access_token = 'fb12d7edd860927ce27467d8ec4aee94-1cb7ffc0e40d649b736315872a10c545'
 model_name = 'lr-v1-avg_score1.454-2000'
 
 # init trading session
@@ -38,54 +40,65 @@
 drop_out = graph.get_tensor_by_name('strided_slice_1:0')
 y_ = graph.get_tensor_by_name('Softmax:0')
 
-# global variables
+# other variables
 log = pd.DataFrame()
-start_time = str(datetime.datetime.now())[:-7].replace(':', '-')
+tz = pytz.timezone('Europe/Vilnius')
+start_time = str(datetime.datetime.now(tz))[:-13].replace(':', '-')
+margin_rate = float(trading_sess.check_account_summary()['account']['marginRate'])
+last_complete_candle_stamp = ''
 
 
 def do_stuff_every_period():
 
     global log
     global start_time
-    current_time = str(datetime.datetime.now())[:-7]
-
-    # gather data and return signal
-    oanda_data = get_latest_oanda_data('EUR_USD', 'H1', 64)
+    global last_complete_candle_stamp
+    global margin_rate
+    current_time = str(datetime.datetime.now(tz))[:-13]
+
+    # estimate position size
+    account_balance = np.around(float(trading_sess.check_account_summary()['account']['balance']), 0)
+    funds_to_commit = account_balance * (1 / margin_rate)
+
+    # download latest data
+    # always check if new candle is present, because even after 5 seconds, it might be not formed if market is very calm
+    while True:
+        oanda_data = get_latest_oanda_data('EUR_USD', 'H1', 300)  # many data-points to increase EMA and such accuracy
+        current_complete_candle_stamp = oanda_data[-1]['time']
+        if current_complete_candle_stamp != last_complete_candle_stamp:  # if new candle is complete
+            break
+        time.sleep(5)
+    last_complete_candle_stamp = current_complete_candle_stamp
+
+    # get features
     input_data_raw, input_data_dummy = get_features(oanda_data)
     input_data, input_data_dummy = remove_nan_rows([input_data_raw, input_data_dummy])
-    input_data_scaled_no_dummies = (input_data - min_max_scaling[1, :]) / (
-            min_max_scaling[0, :] - min_max_scaling[1, :])
-    input_data_scaled = np.concatenate([input_data_scaled_no_dummies, input_data_dummy], axis=1)
+    input_data_scaled_no_dummy = (input_data - min_max_scaling[1, :]) / (min_max_scaling[0, :] - min_max_scaling[1, :])
+    input_data_scaled = np.concatenate([input_data_scaled_no_dummy, input_data_dummy], axis=1)
 
     # estimate signal
-    y_pred = sess.run(y_, feed_dict={x: input_data_scaled, drop_out: 1})
-    order_signal = y_pred.argmax()  # 0 stands for buy, 1 for sell, 2 for hold
-
-    print('{} | price: {:.5f} | signal: buy: {:.2f}, sell: {:.2f}, nothing: {:.2f}'
-          .format(current_time, oanda_data[-1]['closeMid'], y_pred[0][0], y_pred[0][1], y_pred[0][2]))
-
-    # if signal long
-    if order_signal == 0:
-        if trading_sess.order_book['EUR_USD']['order_type'] == -1:
-            trading_sess.close_order('EUR_USD')
-        trading_sess.open_order('EUR_USD', position_size)
-
-    # if signal short
-    elif order_signal == 1:
-        if trading_sess.order_book['EUR_USD']['order_type'] == 1:
+    y_pred = sess.run(y_, feed_dict={x: input_data_scaled[-1:, :], drop_out: 1})
+    order_signal_id = y_pred.argmax()
+    order_signal = [1, -1, 0][order_signal_id]  # 0 stands for buy, 1 for sell, 2 for hold
+
+    # manage trading positions
+    current_position = trading_sess.order_book['EUR_USD']['order_type']
+    if current_position != order_signal:
+        if current_position is not None:
             trading_sess.close_order('EUR_USD')
-        trading_sess.open_order('EUR_USD', -position_size)
-
-    # else (uncharted waters)
+        trading_sess.open_order('EUR_USD', funds_to_commit * order_signal)
     else:
-        print('Do nothing')
+        print('{}: EUR_USD (holding)'.format(['Long', 'Short', 'Nothing'][order_signal_id]))
 
     # log
     new_log = pd.DataFrame([[current_time, oanda_data[-1]['closeMid'], y_pred]],
                            columns=['Datetime', 'Last input Price', 'y_pred'])
     log = log.append(new_log)
     log.to_csv('logs/log {}.csv'.format(start_time))
 
+    print('{} | price: {:.5f} | signal: buy: {:.2f}, sell: {:.2f}, nothing: {:.2f}'
+          .format(current_time, oanda_data[-1]['closeMid'], y_pred[0][0], y_pred[0][1], y_pred[0][2]))
+
 
 # Scheduler
 scheduler = BlockingScheduler()