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# Calculate reward
def get_reward(self):
    self.punish_value += self.net_worth * 0.00001
    if self.episode_orders > 1 and self.episode_orders > self.prev_episode_orders:
        self.prev_episode_orders = self.episode_orders
        if self.trades[-1]['type'] == "buy" and self.trades[-2]['type'] == "sell":
            reward = self.trades[-2]['total']*self.trades[-2]['current_price'] - self.trades[-2]['total']*self.trades[-1]['current_price']
            reward -= self.punish_value
            self.punish_value = 0
            self.trades[-1]["Reward"] = reward

pythonlessons

# sort sell and buy orders, put arrows in appropiate order positions
for trade in trades:
    trade_date = mpl_dates.date2num([pd.to_datetime(trade['Date'])])[0]
    if trade_date in Date_Render_range:
        if trade['type'] == 'buy':
            high_low = trade['Low']-10
            self.ax1.scatter(trade_date, high_low, c='green', label='green', s = 120, edgecolors='none', marker="^")
        else:
            high_low = trade['High']+10
            self.ax1.scatter(trade_date, high_low, c='red', label='red', s = 120, edgecolors='none', marker="v")

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minimum = np.min(np.array(self.render_data)[:,1:])
maximum = np.max(np.array(self.render_data)[:,1:])
RANGE = maximum - minimum

# sort sell and buy orders, put arrows in appropiate order positions
for trade in trades:
    trade_date = mpl_dates.date2num([pd.to_datetime(trade['Date'])])[0]
    if trade_date in Date_Render_range:
        if trade['type'] == 'buy':
            high_low = trade['Low'] - RANGE*0.02

pythonlessons

print("average {} episodes agent net_worth: {}, orders: {}".format(test_episodes, average_net_worth/test_episodes, average_orders/test_episodes))
print("No profit episodes: {}".format(no_profit_episodes))
# save test results to test_results.txt file
with open("test_results.txt", "a+") as results:
    results.write(f'{datetime.now().strftime("%Y-%m-%d %H:%M")}, {name}, test episodes:{test_episodes}')
    results.write(f', net worth:{average_net_worth/(episode+1)}, orders per episode:{average_orders/test_episodes}')
    results.write(f', no profit episodes:{no_profit_episodes}, comment: {comment}\n')

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def save(self, name="Crypto_trader", score="", args=[]):
    # save keras model weights
    self.Actor.Actor.save_weights(f"{self.log_name}/{score}_{name}_Actor.h5")
    self.Critic.Critic.save_weights(f"{self.log_name}/{score}_{name}_Critic.h5")

    # log saved model arguments to file
    if len(args) > 0:
        with open(f"{self.log_name}/log.txt", "a+") as log:
            current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
            log.write(f"{current_time}, {args[0]}, {args[1]}, {args[2]}, {args[3]}, {args[4]}\n")

pythonlessons

if __name__ == "__main__":            
    df = pd.read_csv('./pricedata.csv')
    df = df.sort_values('Date')

    lookback_window_size = 50
    test_window = 720 # 30 days 
    train_df = df[:-test_window-lookback_window_size]
    test_df = df[-test_window-lookback_window_size:]
    
    # Create our custom Neural Networks model

pythonlessons

if __name__ == "__main__":            
    df = pd.read_csv('./pricedata.csv')
    df = df.sort_values('Date')

    lookback_window_size = 50
    test_window = 720 # 30 days 
    train_df = df[:-test_window-lookback_window_size]
    test_df = df[-test_window-lookback_window_size:]

    agent = CustomAgent(lookback_window_size=lookback_window_size, lr=0.00001, epochs=1, optimizer=Adam, batch_size = 32, model="Dense")

pythonlessons

import matplotlib.pyplot as plt
from mplfinance.original_flavor import candlestick_ohlc
import matplotlib.dates as mpl_dates

def Plot_OHCL(df, ax1_indicators=[], ax2_indicators=[]):
    df_original = df.copy()
    # necessary convert to datetime
    df["Date"] = pd.to_datetime(df.Date)
    df["Date"] = df["Date"].apply(mpl_dates.date2num)

pythonlessons

import pandas as pd
from ta.trend import SMAIndicator

def AddIndicators(df):
    # Add Simple Moving Average (SMA) indicators
    df["sma7"] = SMAIndicator(close=df["Close"], window=7, fillna=True).sma_indicator()
    df["sma25"] = SMAIndicator(close=df["Close"], window=25, fillna=True).sma_indicator()
    df["sma99"] = SMAIndicator(close=df["Close"], window=99, fillna=True).sma_indicator()
    
    return df

pythonlessons

import pandas as pd
from ta.volatility import BollingerBands

def AddIndicators(df):
    # Add Bollinger Bands indicator
    indicator_bb = BollingerBands(close=df["Close"], window=20, window_dev=2)
    df['bb_bbm'] = indicator_bb.bollinger_mavg()
    df['bb_bbh'] = indicator_bb.bollinger_hband()
    df['bb_bbl'] = indicator_bb.bollinger_lband()