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T32_v3.py
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T32_v3.py
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import matplotlib.pyplot as plt
import random, shutil, pickle, sys
import numpy as np
from collections import deque
from keras import Sequential
from keras.optimizers import RMSprop
from IPython.display import display
from PIL import Image
import pandas as pd
from keras import Model
import keras, os, glob
import tensorflow as tf
from keras.layers import Layer, Dense, Conv2D, Flatten, RepeatVector,Dropout, LayerNormalization, MultiHeadAttention, GlobalAveragePooling1D
from keras.layers import Activation, LSTM, Bidirectional , Dropout
from keras import layers
# import cv2
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
import codecs
import csv
import secrets
import sqlite3
from IPython.display import display
from IPython.display import clear_output
import time
from tabulate import tabulate
import json
import datetime
from tensorflow.keras.optimizers import Adam
import math
class ChartDecorator:
def __init__(self):
self.balance_limit = 300
self.standard_balance = 1000
self.prev_trade = None
def add_top_bottom_bar(self, img, draw_context):
# Define the coordinates of the black bar
x1, y1 = 0, 204
x2, y2 = 200, 224
# Draw the black bar bottom bar
draw_context.rectangle([x1, y1, x2, y2], fill=(0, 0, 0))
# Draw the black bar top bar
x1, y1 = 0, 0
x2, y2 = 224, 14
draw_context.rectangle([x1, y1, x2, y2], fill=(0, 0, 0))
def draw_account_balance(self, img, draw_context, account_balance = 1000):
balance = self.balance_limit + (account_balance - self.standard_balance)
if balance == self.balance_limit:
x1, y1 = 3, 204
x2, y2 = 75 , 224
# Draw the green bar for account
draw_context.rectangle([x1, y1, x2, y2], fill=(110, 235, 131))
elif balance < self.balance_limit:
health_bar = int((balance * 75) / self.balance_limit )
lost_bar = 75 - health_bar
#issue 1
# print(f"issue 1 balance {balance}")
# print(f"health bar {health_bar}")
# print(f"lost bar {lost_bar}")
#current_balnace
x1, y1 = 3, 204
x2, y2 = health_bar, 224
draw_context.rectangle([x1, y1, x2, y2], fill=(110, 235, 131))
#lost bar
x1, y1 = health_bar, 204
x2, y2 = 75 , 224
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 0, 0))
elif balance > self.balance_limit:
x1, y1 = 3, 204
x2, y2 = 75 , 224
# Draw the green bar for account
draw_context.rectangle([x1, y1, x2, y2], fill=(110, 235, 131))
#Draw the profit section on account_bar
profit = int(((balance - self.balance_limit) * 75) / self.balance_limit)
x1, y1 = 80, 204
x2, y2 = 80 + profit , 224
# Draw the green bar for account
draw_context.rectangle([x1, y1, x2, y2], fill=(0,0,255))# fill=(71, 44, 27))
#draw start separator
#draw Account Separator and end separator
x1, y1 = 0, 204
x2, y2 = 3 , 224
# Draw the green bar for account
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 87, 20))
#draw Account Separator and end separator
x1, y1 = 76, 204
x2, y2 = 80 , 224
# Draw the green bar for account
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 87, 20))
#draw Account Separator and end separator
x1, y1 = 155, 204
x2, y2 = 160 , 224
# Draw the green bar for account
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 87, 20))
def draw_current_trade(self,img, draw_context, position=None):
if position == "buy":
x1, y1 = 160, 204
x2, y2 = 175, 224
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 255, 0))
elif position == "sell":
x1, y1 = 180, 204
x2, y2 = 195, 224
draw_context.rectangle([x1, y1, x2, y2], fill=(0, 255, 0))
else:
x1, y1 = 200, 204
x2, y2 = 215, 224
draw_context.rectangle([x1, y1, x2, y2], fill=(255,0,255))
#Draw position separator icons
x1, y1 = 175, 204
x2, y2 = 180, 224
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 255, 255))
x1, y1 = 195, 204
x2, y2 = 200, 224
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 255, 255))
x1, y1 = 215, 204
x2, y2 = 220, 224
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 255, 255))
def draw_profit_bar(self, img, draw_context, profit = 0, account_balance = 1000, position = None):
pnl_parts = int(abs(profit) / 10)
for segment in range(0, 200, 10):
f = segment + 10
x1, y1 = segment+10, 0
x2, y2 = f + 1, 9
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 255, 255))
#display profit on the chart
if profit > 0 and pnl_parts > 0 and segment >= 100:
if segment < pnl_parts * 10 + 100:
x1, y1 = segment, 0
x2, y2 = segment + 10, 9
draw_context.rectangle([x1, y1, x2, y2], fill=(27, 152, 224))
elif profit < 0 and pnl_parts > 0 and segment <= 90 and segment >= 100 - abs(profit):
x1, y1 = segment, 0
x2, y2 = segment + 10, 9
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 0, 0))
#draw progressive account balance below profit bar
account_balance_bar = (200*account_balance)/2000
x1, y1 = 0, 10
x2, y2 = account_balance_bar, 14
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 119, 0))
#Add a closing indication at the top of the
if position == "close" and profit > 0 and self.prev_trade[2] == 1:
x1, y1 = 200, 0
x2, y2 = 224, 14
draw_context.rectangle([x1, y1, x2, y2], fill=(255, 255, 0))
elif position == "close" and profit > 0 and self.prev_trade == 2:
x1, y1 = 200, 0
x2, y2 = 224, 14
draw_context.rectangle([x1, y1, x2, y2], fill=(0, 255, 0))
elif position == "close" and profit < 0 and self.prev_trade in [2,1]:
x1, y1 = 200, 0
x2, y2 = 224, 14
draw_context.rectangle([x1, y1, x2, y2], fill=(255,0, 0))
def draw_buy_bar(self, img2, start_lines, end_lines, position = None, profit = 0, prev_trade=None, account_balance = 1100 ):
self.prev_trade = prev_trade
from PIL import Image, ImageDraw
d = ImageDraw.Draw(img2)
self.add_top_bottom_bar(img2, d)
self.draw_account_balance(img2, d, account_balance)
if position == "sell" and 2 in [self.prev_trade[3]]:
#draw icon for trade setup
self.draw_current_trade(img2, d, "sell")
fixed_ratio = False
#issue fix bug
#a situation where there is profit but since the trade was entered there were pertuabations of zooming and changing of window prices indicated on the subwindow
#fix this by giving a ratio use profit to check if we are profitable, then check if end lines are lower than start lines , use a ratio of 1px for 3 points
#when scaling the bar check the distance remaining behind you at the top of the window. start from index 14 your calculations
if profit >= 0 and start_lines["ask_line"] > end_lines["ask_line"] :
# print(f"\nRatio bug found {end_lines}\n")
ratio = 15 #int((start_lines["ask_line"] - end_lines["ask_line"] ) / 3)
if end_lines["ask_line"] - ratio < 14:
start_lines["ask_line"] = 14
start_lines["bid_line"] = 18
else:
start_lines["ask_line"] = end_lines["ask_line"] - ratio
start_lines["bid_line"] = end_lines["bid_line"] - ratio
fixed_ratio = True
# Draw indicator of trade start
line_color = (200,200,200)
top = (200, end_lines["ask_line"])
bottom = (200, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=20)
#Draw start marker for the trade
line_color = (0, 255, 0)
top = (200, start_lines["ask_line"])
bottom = (200, start_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=30)
#Draw start line for trade entry
line_color = (0, 255, 0)#(252, 81, 48)
top = (50, start_lines["ask_line"])
bottom = (50, start_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=250)
#Draw profit indicator of sell
if profit < 0:
#Draw the main bar of the trade position
if fixed_ratio:
line_color = (255, int(ratio/2+10), 0)
top = (200, start_lines["ask_line"])
bottom = (200,end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=int(ration/5)+10)
fixed_ratio = False
else:
line_color = (255, 0, 0)
top = (200, start_lines["ask_line"])
bottom = (200,end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
else:
line_color = (0, 255, 0)
top = (200, start_lines["ask_line"])
bottom = (200,end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
#Draw loss indicator for sell
self.draw_profit_bar(img2, d, profit)
elif position == "buy" and 1 in [self.prev_trade[3]]:
#issue fix bug 2
if profit >= 0 and end_lines["ask_line"] > start_lines["ask_line"]:
ratio = 15
if end_lines["bid_line"] + ratio > 204:
start_lines["ask_line"] = 200
start_lines["bid_line"] = 204
else:
start_lines["ask_line"] = end_lines["ask_line"] + ratio
start_lines["bid_line"] = end_lines["bid_line"] + ratio
#draw bottom right trade status sell , buy , close
self.draw_current_trade(img2, d, "buy") #
#Draw indicator of trade start
#Draw end marker
line_color = (65, 64, 102)
top = (200, end_lines["ask_line"])
bottom = (200, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=20)
#Draw start marker for buy
line_color = (255, 255, 0)
top = (200, start_lines["ask_line"])
bottom = (200, start_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=30)
#Draw start buy line across the chart
line_color = (255, 255, 0)#(255,0,200)
top = (50, start_lines["ask_line"])
bottom = (50, start_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=250)
if profit < 0:
line_color = (255, 0, 0)
top = (200, start_lines["ask_line"])
bottom = (200, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
else:
line_color = (255, 255, 0)
top = (200, start_lines["ask_line"])
bottom = (200, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
self.draw_profit_bar(img2, d, profit)
elif position in ["buy", "sell"] and self.prev_trade[3] == 0:
# print(f"Draw buy bar \n\nElse{position}\n\n:")
#draw main vertical bar
self.draw_current_trade(img2, d, "close")
line_color = (255,0,255)
top = (200, start_lines["ask_line"])
bottom = (200, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=15)
#draw end bar
line_color = (255,0,255)
top = (200, end_lines["ask_line"])
bottom = (200, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=30)
#Draw start marker and line
line_color = (255,0,255)
top = (200, start_lines["ask_line"])
bottom = (200, start_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=30)
line_color = (2, 8, 135)
top = (50, start_lines["ask_line"])
bottom = (50, start_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=250)
# self.draw_profit_bar(img2, d, profit)
if self.prev_trade[2] == 2 and profit > 0:
#print right bar for the trade that has closed
line_color = (0, 255, 0)
top = (213, start_lines["ask_line"])
bottom = (213, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
line_color = (241, 254, 198)
top = (190, start_lines["ask_line"])
bottom = (190, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
elif self.prev_trade[2] == 2 and profit < 0:
line_color = (0, 255, 0)
top = (213, start_lines["ask_line"])
bottom = (213, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
line_color = (255,0,0)
top = (190, start_lines["ask_line"])
bottom = (190, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
elif self.prev_trade[2] == 1 and profit > 0:
# print(f"\n\nHey I was here\n\n{self.prev_trade}")
line_color = (255, 255, 0)
top = (213, start_lines["ask_line"])
bottom = (213, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
line_color = (241, 254, 198)
top = (190, start_lines["ask_line"])
bottom = (190, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
elif self.prev_trade[2] == 1 and profit < 0:
line_color = (255, 255, 0)
top = (213, start_lines["ask_line"])
bottom = (213, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
line_color = (255,0,0)
top = (190, start_lines["ask_line"])
bottom = (190, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=10)
else :#:position == "close" and self.prev_trade[2] == 0:
self.draw_current_trade(img2, d, "close")
# print("I run for position Close")
line_color = (255,0,255)
top = (200, end_lines["ask_line"])
bottom = (200, end_lines["bid_line"])
d.line([top, bottom], fill=line_color, width=30)
self.draw_profit_bar(img2, d, profit, account_balance, position)
return img2
# def display_row_images(long_short_dict_now , long_short_dict_next):
from collections import deque
class ForexCustomEnv:
def __init__(self, m1short, m5short,chart_decorator):
self.m1short = m1short
self.m5short = m5short
self.trade_queue = deque(maxlen=4)
self.image_queue = deque(maxlen=4)
self.trade_queue_m5 = deque(maxlen=4)
self.image_queue_m5 = deque(maxlen=4)
self.dataset_directory = "colabM1M5/episode2/"
self.account_balance = 1000
self.env_draw = False
self.chart_decorator = chart_decorator
self.prev_trade = deque(maxlen=4)
self.prev_trade.append(0)
self.prev_trade.append(0)
self.prev_trade.append(0)
self.prev_trade.append(0)
self.current_step = 4
self.action = 0
self.position = "close"
def get_ask_bid_lines(self, image_path):
ask_bid_lines = {"ask_line":None, "bid_line":None, "key": 0}
img = Image.open(f"{self.dataset_directory}{image_path}")
img_data = np.array(img)
last_part = Image.fromarray(img_data[:,200:201,:])
img1_pixels = last_part.load()
ask_color = (255,0,0)
bid_color = (119, 136, 153)
for y in range(224):
if img1_pixels[0,y] == ask_color:
ask_bid_lines["ask_line"] = y
if img1_pixels[0,y] == bid_color:
ask_bid_lines["bid_line"] = y
if ask_bid_lines["ask_line"] == None:
# print("Gotcha")
ask_bid_lines["ask_line"] = ask_bid_lines["bid_line"] + 3
ask_bid_lines["bid_line"] = ask_bid_lines["bid_line"] + 6
return ask_bid_lines , img
def reset(self, current_step = 4):
self.current_step = current_step
linesq, imageq = self.get_ask_bid_lines(f"{self.m1short.iloc[self.current_step - 4]['image_path']}")
linesq["key"] = current_step - 4
linest, imaget = self.get_ask_bid_lines(f"{self.m1short.iloc[self.current_step - 3]['image_path']}")
linest["key"] = current_step - 3
liness, images = self.get_ask_bid_lines(f"{self.m1short.iloc[self.current_step - 2]['image_path']}")
liness["key"] = current_step - 2
linesc, imagec = self.get_ask_bid_lines(f"{self.m1short.iloc[self.current_step - 1]['image_path']}")
linesc["key"] = current_step - 1
linesq_m5, imageq_m5 = self.get_ask_bid_lines(f"{self.m5short.iloc[self.current_step - 4]['image_path']}")
linesq_m5["key"] = current_step - 4
linest_m5, imaget_m5 = self.get_ask_bid_lines(f"{self.m5short.iloc[self.current_step - 3]['image_path']}")
linest_m5["key"] = current_step - 3
liness_m5, images_m5 = self.get_ask_bid_lines(f"{self.m5short.iloc[self.current_step - 2]['image_path']}")
liness_m5["key"] = current_step - 2
linesc_m5, imagec_m5 = self.get_ask_bid_lines(f"{self.m5short.iloc[self.current_step - 1]['image_path']}")
linesc_m5["key"] = current_step - 1
self.trade_queue.append(linesq)
self.trade_queue.append(linest)
self.trade_queue.append(liness)
self.trade_queue.append(linesc)
self.image_queue.append(imageq)
self.image_queue.append(imaget)
self.image_queue.append(images)
self.image_queue.append(imagec)
self.trade_queue_m5.append(linesq_m5)
self.trade_queue_m5.append(linest_m5)
self.trade_queue_m5.append(liness_m5)
self.trade_queue_m5.append(linesc_m5)
self.image_queue_m5.append(imageq_m5)
self.image_queue_m5.append(imaget_m5)
self.image_queue_m5.append(images_m5)
self.image_queue_m5.append(imagec_m5)
self.done = False
self.reward = 0
self.position = "close"
self.reset_current_trade()
self.account_balance = 1000
next_state = self.get_obs()
self.episode_profit = 0
self.prev_trade = deque(maxlen=4)
self.prev_trade.append(0)
self.prev_trade.append(0)
self.prev_trade.append(0)
self.prev_trade.append(0)
self.action = 0
#reset standard limit in chart decorator
self.chart_decorator.standard_limit = self.account_balance
return next_state, self.reward, self.done, self.current_trade
def reset_current_trade(self):
self.current_trade = {}
self.current_trade["current_step"] = self.current_step
self.current_trade["ask_bid_lines"] = None
self.current_trade["ask_bid_lines_m5"] = None
self.current_trade["entry_price"] = None
self.current_trade["current_price"] = None
self.current_trade["timesteps"] = 0
self.current_trade["position"] = "close"
self.current_trade["profit"] = 0
self.current_trade["position"] = None
self.current_trade["parent_ask_bid_lines"] = None
self.current_trade["reward"] = 0
self.current_trade["balance"] = self.account_balance
self.current_trade["episode_profit"] = 0
#this code is to add dimensions to 8 of them 4 for m1short 4 for m5short remaining to draw on m5 charts
self.current_trade["ask_bid_lines_m5"] = None
self.current_trade["parent_ask_bid_lines_m5"] = None
self.current_trade["highest"] = 0
def display_stacked_horizontally(self,imageq, imaget,images,imagec):
# # Calculate the required dimensions for the new image
new_width = imageq.width * 4
new_height = imageq.height
# # Create a new image with the required dimensions
new_image = Image.new("RGB", (new_width, new_height))
# # Paste the individual images side by side
new_image.paste(imageq, (0, 0))
new_image.paste(imaget, (imageq.width, 0))
new_image.paste(images, (imageq.width + imaget.width, 0))
new_image.paste(imagec, (imageq.width + imaget.width + images.width, 0))
def reset_collection(self, t_q, i_q):
_queue = deque(maxlen=4)
for i in range(0, 4):
_lines_c, _image_c = self.get_ask_bid_lines(f"{self.dataset_directory}{self.m1short.iloc[t_q[i]['key']]['image_path']}")
_image_c = self.draw_buy_bar(_image_c, _lines_c, _lines_c, position="close", profit = 0)
_queue.append(_image_c)
return t_q, _queue
def draw_collection(self,q):
# # Calculate the required dimensions for the new image
new_width = q[0].width * 4
new_height =q[0].height
# # Create a new image with the required dimensions
new_image = Image.new("RGB", (new_width, new_height))
# # Paste the individual images side by side
new_image.paste(q[0], (0, 0))
new_image.paste(q[1], (q[0].width, 0))
new_image.paste(q[2], (q[0].width + q[0].width, 0))
new_image.paste(q[3], (q[0].width + q[0].width + q[0].width, 0))
display(new_image)
def draw_buy_bar(self, current_image, start_lines, end_lines, position = None, profit = 0, prev_trade=None):
# self.position = position
# self.profit = profit
return self.chart_decorator.draw_buy_bar(current_image, start_lines, end_lines, self.position, profit, prev_trade, self.account_balance)
'''
@description: Enters a trade in the forex environment
Enters the trade in the current time step, sets the position of the trade. sets the ask bid lines for the current frame
sets the entry price for the trade, and profit = 0. Then draws on the image the entry icon on the chart
and saves the updated current_image with modifications to the image queue with its corresponding ask and bid lines.
then initializes the timesteps of the current trade to 0. sets the reward to 0.2
@params
@position = None the position of the current_trade
@ask_bid_lines of the current_image
@current_image the current frame of the environment
'''
def calculate_reward(self, profit_amount, risk_level, profit_param_a, profit_param_b, risk_param_c, risk_param_d, time_elapsed, discount_factor):
profit_val = math.tanh(profit_param_a * profit_amount - profit_param_b)
scaled_profit_val = profit_param_a * profit_val
risk_val = math.exp(-risk_param_c * risk_level)
time_val = math.exp(-discount_factor * time_elapsed)
return (scaled_profit_val * risk_val * time_val)*10
def get_reward(self, c_trade):
return self.calculate_reward(c_trade["profit"],risk_level=0.9, profit_param_a=0.05, profit_param_b=0.9, risk_param_c=0.000005, risk_param_d=0.5, time_elapsed=c_trade["timesteps"], discount_factor=0.1)
# Define the profit and loss thresholds
# entry_price = c_trade["entry_price"]
# exit_price = c_trade["current_price"]
# highest = c_trade["highest"]
# timesteps = c_trade["timesteps"]
# profit = c_trade["profit"]
# # profit_threshold = highest if highest > 40 else highest + 200
# # Define the risk factor
# volatility = 0.2
# time_to_expire = (timesteps * 60) / 60 # Convert holding period to hours
# risk_factor = (volatility * np.sqrt((timesteps * 60 )/ 60)) + 0.00001
# #reward if profit > 0:
# # #profit > 0 and profit < threshold
# if profit == 0:
# reward = 0.01
# # print(f"GR: if {c_trade} reward {reward}")
# # reversal in trade
# elif highest * 0.80 >= profit and highest > 20:
# # reward = -(1 /( profit/risk_factor*0.001))
# reward = (-1/(((profit/risk_factor)*0.01)))
# print(f"GR: highest ran {c_trade} reward {reward}")
# elif profit > 0 :#and #profit < profit_threshold :
# reward = ((profit/risk_factor)*0.001)
# print(f"GR: Profit > 0, < threshold {c_trade} reward {reward}")
# elif profit < 0:
# reward = ((profit/risk_factor)*0.001) * 9
# # reward = -(1 /( profit/risk_factor*0.001))
# print(f"GR: profit < 0 {c_trade} reward {reward}")
# else:
# reward = 0
# print(f"GR: else {c_trade} reward {reward}")
# # Return the reward
# if reward > 1:
# reward = 0.2 * 0.05
# if reward < -1:
# reward = -1 * 0.05
# return reward
def enter_trade(self, ask_bid_lines, ask_bid_lines_m5,current_image, current_image_m5):
self.current_trade["position"] = self.position
self.current_trade["ask_bid_lines"] = ask_bid_lines
self.current_trade["ask_bid_lines_m5"] = ask_bid_lines_m5
self.current_trade["entry_price"] = float(f"{self.m1short.iloc[self.current_step]['Ask']}") if self.position == "buy" else float(f"{self.m1short.iloc[self.current_step]['Bid']}")
self.current_trade["profit"] = 0
self.current_trade["current_price"] = float(f"{self.m1short.iloc[self.current_step]['Ask']}")
self.current_trade["timesteps"] = 0
self.current_trade["reward"] = 0.4
self.current_trade["parent_ask_bid_lines"] = ask_bid_lines
self.current_trade["parent_ask_bid_lines_m5"] = ask_bid_lines_m5
self.current_trade["balance"] = self.account_balance
self.current_trade["highest"] = 0
#self.current_trade["highest"] += 5
current_image = self.draw_buy_bar(current_image, ask_bid_lines, ask_bid_lines, self.position , self.current_trade["profit"], self.prev_trade )
current_image_m5 = self.draw_buy_bar(current_image_m5, ask_bid_lines_m5, ask_bid_lines_m5, self.position, self.current_trade["profit"], self.prev_trade)
self.trade_queue.append(ask_bid_lines)
self.trade_queue_m5.append(ask_bid_lines_m5)
self.image_queue.append(current_image)
self.image_queue_m5.append(current_image_m5)
if self.env_draw:
self.draw_collection(self.image_queue)
self.draw_collection(self.image_queue_m5)
self.reward = 0#self.account_balance/self.account_balance
def enter_idle_mode(self, ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5 ):
self.current_trade["ask_bid_lines"] = ask_bid_lines
self.current_trade["ask_bid_lines_m5"] = ask_bid_lines_m5
self.current_trade["entry_price"] = float(f"{self.m1short.iloc[self.current_step]['Ask']}")
self.current_trade["current_price"] = float(f"{self.m1short.iloc[self.current_step]['Ask']}")
self.current_trade["position"] = self.position
self.current_trade["reward"] = 0
self.current_trade["timesteps"] += 1
self.current_trade["profit"] = 0
self.current_trade["parent_ask_bid_lines"] = None
self.current_trade["parent_ask_bid_lines_m5"] = None
self.current_trade["balance"] = self.account_balance
current_image = self.draw_buy_bar(current_image, ask_bid_lines, ask_bid_lines, self.position, self.current_trade["profit"], self.prev_trade)
current_image_m5 = self.draw_buy_bar(current_image_m5, ask_bid_lines_m5, ask_bid_lines_m5, self.position, self.current_trade["profit"], self.prev_trade)
self.trade_queue.append(ask_bid_lines)
self.trade_queue_m5.append(ask_bid_lines_m5)
self.image_queue.append(current_image)
self.image_queue_m5.append(current_image_m5)
if self.env_draw:
self.draw_collection(self.image_queue)
self.draw_collection(self.image_queue_m5)
self.reward = 0
def hold_position(self,ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5):
self.current_trade["current_price"] = float(f"{self.m1short.iloc[self.current_step]['Bid']}") if self.position == "buy" else float(f"{self.m1short.iloc[self.current_step]['Ask']}")
self.current_trade["profit"] = self.current_trade["current_price"] - self.current_trade["entry_price"] if self.position == "buy" else self.current_trade["entry_price"] - self.current_trade["current_price"]
self.current_trade["timesteps"] += 1
self.current_trade["balance"] = self.account_balance
self.current_trade["highest"] = self.current_trade["profit"] if self.current_trade["profit"] > self.current_trade["highest"] else self.current_trade["highest"]
#self.current_trade["highest"] -= 15
current_image = self.draw_buy_bar(current_image, self.current_trade["parent_ask_bid_lines"], ask_bid_lines, self.position , self.current_trade["profit"], self.prev_trade)
current_image_m5 = self.draw_buy_bar(current_image_m5, self.current_trade["parent_ask_bid_lines_m5"], ask_bid_lines_m5, self.position, self.current_trade["profit"], self.prev_trade)
self.trade_queue.append(ask_bid_lines)
self.trade_queue_m5.append(ask_bid_lines_m5)
self.image_queue.append(current_image)
self.image_queue_m5.append(current_image_m5)
if self.env_draw:
self.draw_collection(self.image_queue)
self.draw_collection(self.image_queue_m5)
if self.current_trade["profit"] >= 0:
self.reward = self.get_reward(self.current_trade)#(self.account_balance + self.current_trade["profit"])/self.account_balance
self.current_trade["reward"] = self.reward
else:
self.reward = self.get_reward(self.current_trade)#-1 - (self.account_balance + self.current_trade["profit"])/self.account_balance
self.current_trade["reward"] = self.reward
def close_position(self, ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5, prev_trade = None):
# print(f"self.position {self.position}")
self.current_trade["current_price"] = float(f"{self.m1short.iloc[self.current_step]['Bid']}") if self.position == "buy" else float(f"{self.m1short.iloc[self.current_step]['Ask']}")
self.current_trade["profit"] = self.current_trade["current_price"] - self.current_trade["entry_price"] if self.position == "buy" else self.current_trade["entry_price"] - self.current_trade["current_price"]
self.current_trade["highest"] = self.current_trade["profit"] if self.current_trade["profit"] > self.current_trade["highest"] else self.current_trade["highest"]
#self.current_trade["highest"] += 5
current_image = self.draw_buy_bar(current_image, self.parent_ask_bid_lines, ask_bid_lines, self.position, self.current_trade["profit"] , prev_trade)
current_image_m5 = self.draw_buy_bar(current_image_m5, self.parent_ask_bid_lines_m5, ask_bid_lines_m5, self.position, self.current_trade["profit"], prev_trade)
self.trade_queue.append(ask_bid_lines)
self.trade_queue_m5.append(ask_bid_lines_m5)
self.image_queue.append(current_image)
self.image_queue_m5.append(current_image_m5)
self.account_balance += self.current_trade["profit"]
if self.env_draw:
self.draw_collection(self.image_queue)
self.draw_collection(self.image_queue_m5)
if self.current_trade["profit"] >= 0:
self.reward = self.get_reward(self.current_trade)##(self.account_balance + self.current_trade["profit"])/self.account_balance
else:
self.reward = self.get_reward(self.current_trade)#-1 - (self.account_balance + self.current_trade["profit"])/self.account_balance
#self.trade_queue, self.image_queue = self.reset_collection(self.trade_queue,self.image_queue)
# self.account_balance += self.current_trade["profit"]
self.current_trade["current_timestep"] = self.current_step
self.current_trade["timesteps"] += 1
self.current_trade["position"] = "close"
self.current_trade["balance"] = self.account_balance
self.current_trade["episode_profit"] = self.episode_profit
self.position = "close"
self.parent_ask_bid = None
def step(self, action):
self.reward = 0
self.done = False
self.action = action
ask_bid_lines, current_image = self.get_ask_bid_lines(f"{self.m1short.iloc[self.current_step]['image_path']}")
ask_bid_lines_m5, current_image_m5 = self.get_ask_bid_lines(f"{self.m5short.iloc[self.current_step]['image_path']}")
ask_bid_lines["key"] = self.current_step
ask_bid_lines_m5["key"] = self.current_step
if self.position == "close":
if action == 1:
self.position = "buy"
self.prev_trade.append(1)
self.parent_ask_bid_lines = ask_bid_lines
self.parent_ask_bid_lines_m5 = ask_bid_lines_m5
self.enter_trade(ask_bid_lines, ask_bid_lines_m5,current_image, current_image_m5)
elif action == 2:
self.position = "sell"
self.prev_trade.append(2)
self.parent_ask_bid_lines = ask_bid_lines
self.parent_ask_bid_lines_m5 = ask_bid_lines_m5
self.enter_trade(ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5)
elif action == 0:
self.position = "close"
self.prev_trade.append(0)
self.parent_ask_bid_lines = ask_bid_lines
self.parent_ask_bid_lines_m5 = ask_bid_lines_m5
self.enter_idle_mode(ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5)
elif self.position == "buy":
if action == 1:
self.prev_trade.append(1)
self.hold_position(ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5)
elif action in [2]:
self.prev_trade.append(0)
self.close_position(ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5, self.prev_trade)
#fix standard limit and standard balance on chart
if self.account_balance > int((2/3) * self.chart_decorator.balance_limit) + self.chart_decorator.standard_balance:
# print("\n\nReseting the account balance in closing buy trade using 2\n\n the if statement\n\n\n")
self.chart_decorator.standard_balance = self.account_balance
# elif self.chart_decorator.standard_balance * (2/3) > self.account_balance :
# print("\n\nReseting the account balance in closing buy trade using 2\n\n the elif statement\n\n\n")
# self.chart_decorator.standard_balance -= self.chart_decorator.balance_limit
# self.reward = (self.account_balance + self.current_trade["profit"])/1000
# self.current_trade["reward"] = (self.account_balance + self.current_trade["profit"])/1000
elif action in [0]:
self.prev_trade.append(0)
self.close_position(ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5, self.prev_trade)
#fix standard limit and standard balance on chart
if self.account_balance > int((2/3) * self.chart_decorator.balance_limit) + self.chart_decorator.standard_balance:
# print("\n\nReseting the account balance in closing buy trade using 0\n\n the if statement\n\n\n")
self.chart_decorator.standard_balance = self.account_balance
# elif self.chart_decorator.standard_balance * (2/3) > self.account_balance :
# print("\n\nReseting the account balance in closing buy trade using 0\n\n the elif statement\n\n\n")
# self.chart_decorator.standard_balance -= self.chart_decorator.balance_limit
elif self.position == "sell":
if action == 2:
self.prev_trade.append(2)
self.hold_position(ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5)
#this reward is to penalize it not to change the position without closing the position
elif action in [1]:
self.prev_trade.append(0)
self.close_position(ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5, self.prev_trade)
#fix standard limit and standard balance on chart
if self.account_balance > int((2/3) * self.chart_decorator.balance_limit) + self.chart_decorator.standard_balance:
# print("\n\nReseting the account balance in closing sell trade using 1\n\n the if statement\n\n\n")
self.chart_decorator.standard_balance = self.account_balance
# elif self.chart_decorator.standard_balance * (2/3) > self.account_balance :
# print("\n\nReseting the account balance in closing sell trade using 1\n\n the elif statement\n\n\n")
# self.chart_decorator.standard_balance -= self.chart_decorator.balance_limit
# self.reward = (self.account_balance + self.current_trade["profit"])/1000
# self.current_trade["reward"] = (self.account_balance + self.current_trade["profit"])/1000
elif action == 0:
self.prev_trade.append(0)
self.close_position(ask_bid_lines, ask_bid_lines_m5, current_image, current_image_m5, self.prev_trade)
# self.prev_trade.append(0)
#fix standard limit and standard balance on chart
if self.account_balance > int((2/3) * self.chart_decorator.balance_limit) + self.chart_decorator.standard_balance:
# print("\n\nReseting the account balance in closing sell trade using 0\n\n the if statement\n\n\n")
# print(f"Account Balance == {self.account_balance} chart decorator standard_balance {self.chart_decorator.standard_balance}")
self.chart_decorator.standard_balance = self.account_balance
# print(f"updated standard balance == {self.chart_decorator.standard_balance}")
# elif self.chart_decorator.standard_balance * (2/3) > self.account_balance :
# print("\n\nReseting the account balance in closing sell trade using 0\n\n the elif statement\n\n\n")
# self.chart_decorator.standard_balance -= self.chart_decorator.balance_limit
self.current_step+=1
next_state = self.get_obs()
if self.account_balance < 810:
self.done = True
# print(self.trade_queue)
return next_state, self.reward, self.done, self.current_trade
def get_obs(self):
stacked_obs = np.expand_dims(np.stack([
np.asarray(self.image_queue_m5[0])/255,
np.asarray(self.image_queue_m5[1])/255,
np.asarray(self.image_queue_m5[2])/255,
np.asarray(self.image_queue_m5[3])/255,
np.asarray(self.image_queue[0])/255,
np.asarray(self.image_queue[1])/255,
np.asarray(self.image_queue[2])/255,
np.asarray(self.image_queue[3])/255
]), axis=0)
# stacked_obs_m5 = np.expand_dims(np.stack([np.asarray(self.image_queue_m5[0])/255, np.asarray(self.image_queue_m5[1])/255, np.asarray(self.image_queue_m5[2])/255, np.asarray(self.image_queue_m5[3])/255]), axis=0)
# stacked = np.concatenate([stacked_obs_m5, stacked_obs], axis=0)
return np.expand_dims(stacked_obs, axis=0)
class TimeSformerBlock(tf.keras.layers.Layer):
def __init__(self, hidden_dim, num_heads):
super(TimeSformerBlock, self).__init__()
# Define the self-attention layer
self.self_attention = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=hidden_dim)
# Define the feedforward layer
self.feedforward = tf.keras.Sequential([
tf.keras.layers.Dense(hidden_dim * 4, activation='relu'),
tf.keras.layers.Dense(hidden_dim)
])
# Define the layer normalization layers
self.norm1 = tf.keras.layers.LayerNormalization()
self.norm2 = tf.keras.layers.LayerNormalization()
def call(self, x):
# Apply layer normalization and self-attention
norm_x = self.norm1(x)
attention_output = self.self_attention(norm_x, norm_x)
x = x + attention_output
# Apply layer normalization and feedforward layer
norm_x = self.norm2(x)
feedforward_output = self.feedforward(norm_x)
x = x + feedforward_output
return x
class Agent:
def __init__(self, state_size, action_size, env, learning_rate=0.001):
self.state_size = state_size
self.action_size = action_size
self.learning_rate = learning_rate
self.epsilon = 0.001
self.epsilon_min = 0.01
self.epsilon_decay = 0.99
self.gamma = 0.99
self.env = env
self.actor = self._actor_model()
self.critic = self._critic_model()
# self.critic.set_weights(self.actor.get_weights())
self.models_dir = "models/attention/"
self.actor_model_name = "T32_v4R_att_actor.h5"
self.critic_model_name = "T32_v4R_att_critic.h5"
self.actor.summary()
def _actor_model(self):
cnn = Sequential()
cnn.add(tf.keras.layers.Conv3D(32, (3,3,3), strides=(1,4,4), padding="same"))
cnn.add(Activation('relu'))
cnn.add(tf.keras.layers.MaxPooling3D(pool_size=(1,2,2), strides=(1,2,2), padding='same'))
cnn.add(tf.keras.layers.Conv3D(64, (3,3,3), padding="same"))
cnn.add(Activation('relu'))
cnn.add(tf.keras.layers.MaxPooling3D(pool_size=(1,2,2), strides=(1,2,2), padding='same'))
cnn.add(tf.keras.layers.Conv3D(64, (3,3,3), padding="same"))
cnn.add(Activation('relu'))
cnn.add(Flatten())
cnn.add(Dense(512, activation='relu'))
transformer = TimeSformerBlock( num_heads=8, hidden_dim=512)
model=Sequential()
model.add(tf.keras.layers.TimeDistributed(cnn,input_shape=self.state_size))
model.add(transformer)
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(self.action_size, activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer=Adam(learning_rate=1e-4))
return model
def _critic_model(self):
cnn = Sequential()
cnn.add(tf.keras.layers.Conv3D(32, (3,3,3), strides=(1,4,4), padding="same"))
cnn.add(Activation('relu'))
cnn.add(tf.keras.layers.MaxPooling3D(pool_size=(1,2,2), strides=(1,2,2), padding='same'))
cnn.add(tf.keras.layers.Conv3D(64, (3,3,3), padding="same"))
cnn.add(Activation('relu'))
cnn.add(tf.keras.layers.MaxPooling3D(pool_size=(1,2,2), strides=(1,2,2), padding='same'))
cnn.add(tf.keras.layers.Conv3D(64, (3,3,3), padding="same"))
cnn.add(Activation('relu'))
cnn.add(Flatten())
cnn.add(Dense(512, activation='relu'))
transformer = TimeSformerBlock( num_heads=8, hidden_dim=512)
model=Sequential()
model.add(tf.keras.layers.TimeDistributed(cnn,input_shape=self.state_size))
model.add(transformer)
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(self.action_size, activation='softmax'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse',optimizer=Adam(learning_rate=1e-4))
return model
def predict(self, state):
return self.actor.predict(state)
def save(self):
self.actor.save(f"{self.models_dir}{self.actor_model_name}")
self.critic.save(f"{self.models_dir}{self.critic_model_name}")
def act(self, state):
if np.random.rand() <= self.epsilon:
return secrets.randbelow(self.action_size) if np.random.choice(20) % 2 == 0 else np.random.choice(self.action_size) , [[0,0,0]]
else:
pred = self.actor.predict(state)
return np.argmax(pred[0][0]), pred
def load(self):
self.actor.load_weights(f"{self.models_dir}{self.actor_model_name}")
self.critic.load_weights(f"{self.models_dir}{self.critic_model_name}")
def test_agent(self, draw=False):
header = ["step","action", "reward", "done","profit", "Episode Profit", "balance", "Eprice", "Cprice", ]
state, reward, done, current_trade = self.env.reset(800)
profits = 0
clear_output(wait=True)
chart_decorator = ChartDecorator()
env = ForexCustomEnv(m1short,m5short, chart_decorator)
self.env = env
self.env.env_draw = draw
# agent = Agent(state_size, action, env)
self.env.account_balance = 1000
self.epsilon = 0.001#1.0
state, reward, done, _ = self.env.reset(4)
for step in range(4, 1200, 1):
rows = []
action, pred = self.act(state)
value = self.critic.predict(state)[0]
next_state, reward, done, current_trade = self.env.step(action)
if action == 0:
profits += current_trade["profit"]
row = [step, action, reward,done, current_trade["profit"], profits , current_trade["balance"], current_trade["entry_price"], current_trade["current_price"]]
rows.append(row)
print(tabulate(rows, headers=header, tablefmt='grid'))
print(f"actor {pred} critic {value}")
# print(f"current_trade {current_trade}\n")
state = next_state
# profits.append(current_trade["profit"])
def train(self, num_episodes, draw = False, epsilon = 0.001):
#here we train the network on all zeros
#we train it on penalties by greedy exporation
#we train it on expert advise
#we let the network train on itself finally and evaluate it
header = ["episode","epsilon", "counter","action", "done" ,"profit", "balance","Eprice","Cprice", "critic target_f_values","target_f__reward", "actor pred qvls", "fit actor q_values", "total_profits"]
self.episode_rewards = []
target_f_values = []
for episode in range(num_episodes):
clear_output(wait=True)
chart_decorator = ChartDecorator()
env = ForexCustomEnv(m1short,m5short, chart_decorator)
self.env = env
# agent = Agent(state_size, action, env)
self.env.account_balance = 1000
self.epsilon = epsilon
self.env.env_draw = draw
state, reward, done, _ = self.env.reset()
total_rewards = 0
counter = 0
total_profits = 0
while not done:
rows = []
action, pred = self.act(state)
next_state, reward, done, current_trade = self.env.step(action)
total_rewards += reward
total_profits += current_trade["profit"]
if not done:
target_f_values = self.critic.predict(next_state)
target_f = reward + self.gamma * target_f_values[0][0]
else:
target_f = reward
q_values = self.actor.predict(state)