def setUp(self) -> None:
"""Set up the Indicator Client."""
# Grab configuration values.
config = ConfigParser()
config.read('configs/config.ini')
CLIENT_ID = config.get('main', 'CLIENT_ID')
REDIRECT_URI = config.get('main', 'REDIRECT_URI')
CREDENTIALS_PATH = config.get('main', 'JSON_PATH')
# Create a robot.
self.robot = PyRobot(client_id=CLIENT_ID,
redirect_uri=REDIRECT_URI,
credentials_path=CREDENTIALS_PATH)
# Grab historical prices, first define the start date and end date.
start_date = datetime.today()
end_date = start_date - timedelta(days=30)
# Grab the historical prices.
historical_prices = self.robot.grab_historical_prices(
start=end_date,
end=start_date,
bar_size=1,
bar_type='minute',
symbols=['AAPL', 'MSFT'])
# Convert data to a Data Frame.
self.stock_frame = self.robot.create_stock_frame(
data=historical_prices['aggregated'])
# Create the indicator client.
self.indicator_client = Indicators(price_data_frame=self.stock_frame)
def golden_cross(trading_robot: PyRobot, indicator_client: Indicators):
"""Set the indicators to reflect the golden cross strategy"""
indicator_client.ema(period=20, column_name='ema_20')
indicator_client.ema(period=200, column_name='ema_200')
# Add a Signal Check
indicator_client.set_indicator_signal_compare(indicator_1='ema_20',
indicator_2='ema_200',
condition_buy=operator.ge,
condition_sell=operator.le)
def get_and_process_data(self, tickers: [str]):
# Pull data
# self.get_data('TDA', tickers=tickers)
# Clean data
# Todo implement data cleaning
# Store data
end_date = datetime.today()
start_date = end_date - timedelta(days=20)
# Get historical data
historical_prices = self.grab_historical_prices(start=start_date,
end=end_date,
bar_size=1,
bar_type='minute')
# todo clean data
# Create stock frame
self.portfolio.stock_frame = self.create_stock_frame(
data=historical_prices['aggregated'])
# Fill in missing values
# todo fill in missing values
# Calculate MACD, RSI, and VWAP
self._indicator_client = Indicators(
price_data_frame=self.portfolio.stock_frame)
conf_val.calculate_columns(
self._indicator_client,
owned=False,
trading_symbol=self._tickers[0],
bot_account=self.get_accounts(account_number=ACCOUNT_NUMBER))
# todo insert signals data into a column on stock frame
# Insert into db
self.insert_into_db(table=tickers[0],
stock_frame=self.portfolio.stock_frame)
# Convert data to a Data Frame.
stock_frame = trading_robot.create_stock_frame(
data=historical_prices['aggregated'])
# We can also add the stock frame to the Portfolio object.
trading_robot.portfolio.stock_frame = stock_frame
# Additionally the historical prices can be set as well.
trading_robot.portfolio.historical_prices = historical_prices
# Portfolio Variance
pprint.pprint(trading_robot.portfolio.portfolio_metrics())
# Create an indicator Object.
indicator_client = Indicators(price_data_frame=stock_frame)
# Add the RSI Indicator.
indicator_client.rsi(period=14)
# Add the 200 day simple moving average.
indicator_client.sma(period=200)
# Add the 50 day exponentials moving average.
indicator_client.ema(period=50)
# Add a signal to check for.
indicator_client.set_indicator_signal(indicator='rsi',
buy=40.0,
sell=20.0,
condition_buy=operator.ge,
end=start_date,
bar_size=1,
bar_type='minute'
)
# Convert data to a Data Frame.
stock_frame = trading_robot.create_stock_frame(data=historical_prices['aggregated'])
# We can also add the stock frame to the Portfolio object.
trading_robot.portfolio.stock_frame = stock_frame
# Additionally the historical prices can be set as well.
trading_robot.portfolio.historical_prices = historical_prices
# Create an indicator Object.
indicator_client = Indicators(price_data_frame=stock_frame)
# Add the RSI Indicator.
indicator_client.rsi(period=14)
# Add the 200 day simple moving average.
indicator_client.sma(period=200)
# Add the 50 day exponentials moving average.
indicator_client.ema(period=50)
# Add the Bollinger Bands.
indicator_client.bollinger_bands(period=20)
# Add the Rate of Change.
indicator_client.rate_of_change(period=1)
trading_robot_portfolio = trading_robot.create_portfolio()
trading_symbol = 'CCIV'
trading_robot_portfolio.add_position(
symbol=trading_symbol,
quantity=1,
asset_type='equity',
)
# Set historical prices for current positions
set_historical_prices(trading_robot)
# todo create a new dataframe that is cleaned/normalized for the models
# Create new indicator client
indicator_client = Indicators(price_data_frame=trading_robot.portfolio.stock_frame)
# Set the confirmation validation strategy
conf_val.define_strat(trading_robot, indicator_client)
pd.set_option('display.max_columns', None)
# Create a new Trade Object for Entering position
new_enter_trade = trading_robot.create_trade(
trade_id='long_enter',
enter_or_exit='enter',
long_or_short='long',
order_type='mkt'
)
# Add an Order Leg
new_enter_trade.instrument(
symbol=trading_symbol,
def define_strat(trading_robot: PyRobot, indicator_client: Indicators): """Set the indicators to reflect the confirmation/validation strategy""" indicator_client.ema(period=20, column_name='ema_20') indicator_client.ema(period=200, column_name='ema_200') indicator_client.rsi(period=14)
def calculate_columns(indicator_client: Indicators, owned: bool, trading_symbol: str,
bot_account: dict):
"""Sets buy/sell/hold signals for the conf_val strategy"""
indicator_client.ema(period=20, column_name='ema_20').dropna(inplace=True)
indicator_client.ema(period=200, column_name='ema_200').dropna(inplace=True)
indicator_client.rsi(period=14).dropna(inplace=True)
indicator_client.macd().dropna(inplace=True) # Defaults to fast: 12, slow: 26, column_name: macd
# Calculate the current % gap between the open and current ema_20
# This will be used to prevent a buy signal from happening if the open just barely
# goes over the ema_20.
v1 = indicator_client.price_data_frame['open']
v2 = indicator_client.price_data_frame['ema_20']
v3 = indicator_client.price_data_frame['ema_200']
indicator_client.price_data_frame['open_ema_20_percent_diff'] = \
((v1 - v2) / abs(v2)) * 100
indicator_client.price_data_frame['ema_20_ema_200_percent_diff'] = \
((v2 - v3) / abs(v3)) * 100
indicator_client.price_data_frame['signals'] = indicator_client.price_data_frame.apply(lambda row: calc_sig(row, owned), axis=1)
print("break")
end=start_date,
bar_size=1,
bar_type='minute')
# Convert data to a Data Frame.
stock_frame = trading_robot.create_stock_frame(
data=historical_prices['aggregated'])
# We can also add the stock frame to the Portfolio object.
trading_robot.portfolio.stock_frame = stock_frame
# Additionally the historical prices can be set as well.
trading_robot.portfolio.historical_prices = historical_prices
# Create an indicator Object.
indicator_client = Indicators(price_data_frame=stock_frame)
# Add the RSI Indicator.
indicator_client.rsi(period=14)
# Add the 200 day simple moving average.
indicator_client.sma(period=200)
# Add the 50 day exponentials moving average.
indicator_client.ema(period=50)
# Add the Bollinger Bands.
indicator_client.bollinger_bands(period=20)
# Add the Rate of Change.
indicator_client.rate_of_change(period=1)
class PyRobotIndicatorTest(TestCase):
"""Will perform a unit test for the Indicator Object."""
def setUp(self) -> None:
"""Set up the Indicator Client."""
# Grab configuration values.
config = ConfigParser()
config.read('configs/config.ini')
CLIENT_ID = config.get('main', 'CLIENT_ID')
REDIRECT_URI = config.get('main', 'REDIRECT_URI')
CREDENTIALS_PATH = config.get('main', 'JSON_PATH')
# Create a robot.
self.robot = PyRobot(
client_id = CLIENT_ID,
redirect_uri = REDIRECT_URI,
credentials_path = CREDENTIALS_PATH
)
# Grab historical prices, first define the start date and end date.
start_date = datetime.today()
end_date = start_date - timedelta(days=30)
# Grab the historical prices.
historical_prices = self.robot.grab_historical_prices(
start=end_date,
end=start_date,
bar_size=1,
bar_type='minute',
symbols=['AAPL','MSFT']
)
# Convert data to a Data Frame.
self.stock_frame = self.robot.create_stock_frame(data=historical_prices['aggregated'])
# Create the indicator client.
self.indicator_client = Indicators(price_data_frame=self.stock_frame)
def test_creates_instance_of_session(self):
"""Create an instance and make sure it's a StockFrame."""
self.assertIsInstance(self.stock_frame, StockFrame)
self.assertIsInstance(self.indicator_client, Indicators)
def test_price_frame_data_property(self):
"""Test getting the Price Data Frame."""
self.assertIsNotNone(self.indicator_client.price_data_frame)
def test_is_multi_index_property(self):
"""Test getting the Price Data Frame."""
self.assertTrue(self.indicator_client.is_multi_index)
def test_change_in_price(self):
"""Test adding the Change in Price."""
# Create the Change in Price indicator.
self.indicator_client.change_in_price()
# Check if we have the column.
self.assertIn('change_in_price', self.stock_frame.frame.columns)
# And that it's not empty.
self.assertFalse(self.stock_frame.frame['change_in_price'].empty)
def test_rsi(self):
"""Test adding the Relative Strength Index."""
# Create the RSI indicator.
self.indicator_client.rsi(period=14)
# Check if we have the column.
self.assertIn('rsi', self.stock_frame.frame.columns)
# And that it's not empty.
self.assertFalse(self.stock_frame.frame['rsi'].empty)
def test_sma(self):
"""Test adding the Simple Moving Average."""
# Create the SMA indicator.
self.indicator_client.sma(period=200)
# Check if we have the column.
self.assertIn('sma', self.stock_frame.frame.columns)
# And that it's not empty.
self.assertFalse(self.stock_frame.frame['sma'].empty)
def test_ema(self):
"""Test adding the Exponential Moving Average."""
# Create the EMA indicator.
self.indicator_client.ema(period=50)
# Check if we have the column.
self.assertIn('ema', self.stock_frame.frame.columns)
# And that it's not empty.
self.assertFalse(self.stock_frame.frame['ema'].empty)
def test_indicator_exist(self):
"""Test checkinf if an indicator column exist."""
# Create the EMA indicator.
self.indicator_client.ema(period=50)
# Check if we have the column.
self.assertIn('ema', self.stock_frame.frame.columns)
# And that it's not empty.
self.assertTrue(self.stock_frame.do_indicator_exist(column_names=['ema']))
def test_indicator_signal(self):
"""Test checkinf if an indicator column exist."""
# Create the EMA indicator.
self.indicator_client.ema(period=50)
self.indicator_client.set_indicator_signal(
indicator='sma',
buy=50.0,
sell=30.0,
condition_buy=operator.ge,
condition_sell=operator.le
)
func_1 = operator.ge
func_2 = operator.le
correct_dict = {
'buy': 50.0,
'sell': 30.0,
'buy_operator': func_1,
'sell_operator': func_2
}
correct_dict_all = {
'sma':{
'buy': 50.0,
'sell': 30.0,
'buy_operator': func_1,
'sell_operator': func_2
}
}
# And that it's not empty.
self.assertDictEqual(
self.indicator_client.get_indicator_signal(indicator='sma'),
correct_dict
)
# And that it's not empty.
self.assertDictEqual(
self.indicator_client.get_indicator_signal(),
correct_dict_all
)
def tearDown(self) -> None:
"""Teardown the Indicator object."""
self.stock_frame = None
self.indicator_client = None