def ws_message(self, ws, message):
# Liquidate position if market is closing soon
if self.__market.is_market_closing():
self.close_position_before_market_close()
# Close graphs
if self.p is not None:
self.p.terminate()
self.p.join()
return
# Convert retrieved data to python dict
message = json.loads(message)
self.ws.on_message(ws, message)
# Get the required data: close price, high, low, volume, time
bar_data = message['data']
close_price = round(bar_data['c'], 2)
close_time = datetime.fromtimestamp(int(str(
bar_data['e'])[:-3])).strftime("%H:%M")
high_price = round(bar_data['h'], 2)
low_price = round(bar_data['l'], 2)
volume = bar_data['v']
print(self.df[-14:])
# Calc MFI
mfi = self.calc_mfi(self.df[-14:])
# New Entry
new_entry = {
'Date': close_time,
'Close': close_price,
'High': high_price,
'Low': low_price,
'Volume': volume,
'buy_signal': np.nan,
'sell_signal': np.nan,
'MFI': mfi
}
# Get buy or sell signal
self.buy_sell(new_entry)
# Add Entry to file
new_entry_str = '{},{},{},{},{},{},{},{}'.format(
close_time, close_price, new_entry['High'], new_entry['Low'],
new_entry['Volume'], new_entry['buy_signal'],
new_entry['sell_signal'], mfi)
# Add entry to file
file_manager.append_to_file('temp_files/{}.csv'.format(self.symbol),
new_entry_str)
# Add entry to dataframe
self.df = self.df.append(new_entry, ignore_index=True)
# Print retrieved data + MFI
print('{}\t{}\tMFI: {}'.format(close_time, close_price, mfi))
def ws_message(self, ws, message):
# Liquidate position
if self.__market.is_market_closing():
self.close_position_before_market_close()
# Close graphs
if self.p is not None:
self.p.terminate()
self.p.join()
return
# Convert data to python dict
message = json.loads(message)
self.ws.on_message(ws, message)
# Get the required data: time, close price
bar_data = message['data']
close_price = bar_data['c']
close_time = datetime.fromtimestamp(int(str(
bar_data['e'])[:-3])).strftime("%H:%M")
# Add the received price in the list
self.closes.append(close_price)
# Calc metrics
sma30 = self.calc_metrics()[0]
sma100 = self.calc_metrics()[1]
# New Entry
new_entry = {
'Date': close_time,
'Close': close_price,
'SMA30': sma30,
'SMA100': sma100,
'buy_signal': np.nan,
'sell_signal': np.nan
}
# Get Signal
self.buy_sell(new_entry)
# Add Entry to file
new_entry_str = '{},{},{},{},{},{} '.format(close_time, close_price,
new_entry['buy_signal'],
new_entry['sell_signal'],
sma30, sma100)
file_manager.append_to_file('temp_files/{}.csv'.format(self.symbol),
new_entry_str)
# Add entry to dataframe
self.df = self.df.append(new_entry, ignore_index=True)
# Print data on the screen
print('{}\tPrice: {}\tSMA30: {}\tSMA100: {}'.format(
close_time, close_price, new_entry['SMA30'], new_entry['SMA100']))
def ws_message(self, ws, message):
from datetime import datetime
# Liquidate position if market is closing soon
if self.__market.is_market_closing():
self.close_position_before_market_close()
# Close graphs
if self.p is not None:
self.p.terminate()
self.p.join()
return
# Convert retrieved data to python dict
msg = json.loads(message)
self.ws.on_message(ws, msg)
# Get the required data: close price, time
bar_data = msg['data']
close_price = bar_data['p']
# Append close price to the list
self.closes.append(close_price)
# increase counter
self.counter += 1
# Calculate the RSI
rsi = self.calc_rsi()
# New entry
new_entry = {
'Date': self.counter,
'Close': close_price,
'RSI': rsi,
'buy_signal': np.nan,
'sell_signal': np.nan
}
# Get buy or sell signal
self.buy_sell(new_entry)
# Add entry to file
new_entry_str = '{},{},{},{},{}'.format(self.counter, close_price,
new_entry['buy_signal'],
new_entry['sell_signal'], rsi)
file_manager.append_to_file('temp_files/{}.csv'.format(self.symbol),
new_entry_str)
# Add entry to dataframe
self.df = self.df.append(new_entry, ignore_index=True)
# Print the received data + RSI
print('{}\tPrice: {}\tRSI: {}'.format(self.counter, close_price,
str(rsi)))
def ws_message(self, ws, message):
# Liquidate position
if self.__market.is_market_closing():
self.close_position_before_market_close()
# Close graphs
if self.p is not None:
self.p.terminate()
self.p.join()
return
# Convert data to python dict
message = json.loads(message)
self.ws.on_message(ws, message)
# Get the required data: close price
bar_data = message['data']
close_price = bar_data['p']
self.counter += 1
# Add the received price in the list
self.closes.append(close_price)
# Calc metrics
sma = self.calc_metrics()[0]
std = self.calc_metrics()[1]
upper = self.calc_metrics()[2]
lower = self.calc_metrics()[3]
if sma == 0 or std == 0 or upper == 0 or lower == 0:
pass
else:
# New entry
new_entry = {
'Date': self.counter,
'Close': close_price,
'SMA': sma,
'STD': std,
'Upper': upper,
'Lower': lower,
'buy_signal': np.nan,
'sell_signal': np.nan
}
# Get signal
self.buy_sell(new_entry)
# Add entry to file
new_entry_str = '{},{},{},{},{},{},{},{}'.format(
self.counter, close_price,
new_entry['buy_signal'], new_entry['sell_signal'],
new_entry['SMA'], new_entry['STD'],
new_entry['Upper'], new_entry['Lower']
)
file_manager.append_to_file('temp_files/{}.csv'.format(self.symbol), new_entry_str)
# Add entry to dataframe
self.df = self.df.append(new_entry, ignore_index=True)
# Print data on the screen
print('{}\tPrice: ${}\tUpper band: {}\tLower band: {}'.format(
self.counter, close_price,
new_entry['Upper'], new_entry['Lower']
))