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Add new features
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‎.gitignore

+1
Original file line numberDiff line numberDiff line change
@@ -131,3 +131,4 @@ dmypy.json
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pyrobot/config.py
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tests/config.py
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.vscode/settings.json
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indicators/

‎pyrobot/indicators.py

+206-2
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,210 @@
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import numpy as np
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import pandas as pd
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14
from typing import Tuple
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from typing import List
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from typing import Iterable
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5-
class Indicator():
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pass
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from pyrobot.stock_frame import StockFrame
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class Indicators():
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def __init__(self, price_data_frame: StockFrame) -> None:
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"""Initalizes the Indicator Client.
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Arguments:
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----
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price_data_frame {pyrobot.StockFrame} -- The price data frame which is used to add indicators to.
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At a minimum this data frame must have the following columns: `['timestamp','close','open','high','low']`.
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Usage:
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----
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>>> historical_prices_df = trading_robot.grab_historical_prices(
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start=start_date,
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end=end_date,
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bar_size=1,
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bar_type='minute'
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)
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>>> price_data_frame = pd.DataFrame(data=historical_prices)
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>>> indicator_client = Indicators(price_data_frame=price_data_frame)
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>>> price_data_frame_indicators = indicator_client.price_data_frame
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"""
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self._price_data_frame: StockFrame = price_data_frame
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self._price_groups = self._price_data_frame.symbol_groups
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if self.is_multi_index:
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True
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@property
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def price_data_frame(self) -> pd.DataFrame:
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return self._price_data_frame.frame
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@price_data_frame.setter
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def price_data_frame(self, price_data_frame):
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self._price_data_frame = price_data_frame
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@price_data_frame.deleter
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def price_data_frame(self):
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del self._price_data_frame
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@property
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def is_multi_index(self):
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if isinstance(self._price_data_frame.frame.index, pd.MultiIndex):
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return True
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else:
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return False
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def change_in_price(self) -> pd.DataFrame:
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"""Calculates the Change in Price.
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Returns:
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----
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pd.DataFrame -- A data frame with the Change in Price.
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"""
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self._price_data_frame.frame['change_in_price'] = self._price_data_frame.frame.groupby(
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by='symbol',
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as_index=False
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)['close'].transform(
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lambda x: x.diff()
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)
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def rsi(self, period: int, method: str = 'wilders') -> pd.DataFrame:
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"""Calculates the Relative Strength Index (RSI).
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Arguments:
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----
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period {int} -- The number of periods to use to calculate the RSI.
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Keyword Arguments:
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----
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method {str} -- The calculation methodology. (default: {'wilders'})
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Returns:
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----
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pd.DataFrame -- A Pandas data frame with the RSI indicator included.
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Usage:
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----
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>>> historical_prices_df = trading_robot.grab_historical_prices(
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start=start_date,
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end=end_date,
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bar_size=1,
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bar_type='minute'
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)
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>>> price_data_frame = pd.DataFrame(data=historical_prices)
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>>> indicator_client = Indicators(price_data_frame=price_data_frame)
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>>> indicator_client.rsi(period=14)
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>>> price_data_frame = inidcator_client.price_data_frame
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"""
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# Define the price data frame.
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price_frame = self._price_data_frame.frame
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# First calculate the Change in Price.
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if 'change_in_price' not in price_frame.columns:
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self.change_in_price()
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# Define the up days.
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price_frame['up_day'] = price_frame.groupby(
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by='symbol',
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as_index=False
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)['change_in_price'].transform(lambda x : np.where(x >= 0, x, 0))
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# Define the down days.
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price_frame['down_day'] = price_frame.groupby(
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by='symbol',
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as_index=False
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)['change_in_price'].transform(lambda x : np.where(x < 0, x.abs(), 0))
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# Calculate the EWMA (Exponential Weighted Moving Average), meaning older values are given less weight compared to newer values.
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price_frame['ewma_up'] = price_frame.groupby('symbol')['up_day'].transform(lambda x: x.ewm(span = period).mean())
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price_frame['ewma_down'] = price_frame.groupby('symbol')['down_day'].transform(lambda x: x.ewm(span = period).mean())
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# Calculate the Relative Strength
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relative_strength = price_frame['ewma_up'] / price_frame['ewma_down']
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# Calculate the Relative Strength Index
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relative_strength_index = 100.0 - (100.0 / (1.0 + relative_strength))
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# Add the info to the data frame.
134+
price_frame['rsi'] = np.where(relative_strength_index == 0, 100, 100 - (100 / (1 + relative_strength_index)))
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# Clean up before sending back.
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price_frame.drop(labels=['ewma_up', 'ewma_down', 'down_day', 'up_day', 'change_in_price'], axis=1, inplace=True)
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# Reassign?
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# self.price_data_frame(price_frame)
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return price_frame
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def sma(self, period: int) -> pd.DataFrame:
145+
"""Calculates the Simple Moving Average (SMA).
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Arguments:
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----
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period {int} -- The number of periods to use when calculating the SMA.
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Returns:
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----
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pd.DataFrame -- A Pandas data frame with the SMA indicator included.
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Usage:
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----
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>>> historical_prices_df = trading_robot.grab_historical_prices(
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start=start_date,
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end=end_date,
160+
bar_size=1,
161+
bar_type='minute'
162+
)
163+
>>> price_data_frame = pd.DataFrame(data=historical_prices)
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>>> indicator_client = Indicators(price_data_frame=price_data_frame)
165+
>>> indicator_client.sma(period=100)
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>>> price_data_frame = inidcator_client.price_data_frame
167+
"""
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# Grab the Price Frame.
170+
price_frame = self._price_data_frame.frame
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# Add the SMA
173+
price_frame['sma'] = price_frame.groupby('symbol')['close'].transform(lambda x: x.rolling(window=period).mean())
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return price_frame
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def ema(self, period: int, alpha: float = 0.0) -> pd.DataFrame:
178+
"""Calculates the Exponential Moving Average (EMA).
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Arguments:
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----
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period {int} -- The number of periods to use when calculating the EMA.
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Returns:
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----
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pd.DataFrame -- A Pandas data frame with the EMA indicator included.
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Usage:
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----
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>>> historical_prices_df = trading_robot.grab_historical_prices(
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start=start_date,
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end=end_date,
193+
bar_size=1,
194+
bar_type='minute'
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)
196+
>>> price_data_frame = pd.DataFrame(data=historical_prices)
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>>> indicator_client = Indicators(price_data_frame=price_data_frame)
198+
>>> indicator_client.ema(period=50)
199+
>>> price_data_frame = inidcator_client.price_data_frame
200+
"""
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# Grab the Price Frame.
203+
price_frame = self._price_data_frame.frame
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# Add the EMA
206+
price_frame['ema'] = price_frame.groupby('symbol', sort=True)['close'].transform(
207+
lambda x: x.ewm(span=period).mean()
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)
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return price_frame

‎pyrobot/portfolio.py

+6-6
Original file line numberDiff line numberDiff line change
@@ -8,7 +8,8 @@ def __init__(self, account_number: str = None) -> None:
88
"""Initalizes a new instance of the Portfolio object.
99
1010
Keyword Arguments:
11-
account_number {str} -- An accout number to associate with the Portfolio. (default: {None})
11+
----
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account_number {str} -- An accout number to associate with the Portfolio. (default: {None})
1213
"""
1314

1415
self.positions: dict = {}
@@ -93,8 +94,7 @@ def add_positions(self, positions: List[dict]) -> dict:
9394
else:
9495
raise TypeError('Positions must be a list of dictionaries.')
9596

96-
def add_position(self, symbol: str, asset_type: str, quantity: int = 0, purchase_price: float = 0.00,
97-
purchase_date: str = None) -> dict:
97+
def add_position(self, symbol: str, asset_type: str, quantity: int = 0, purchase_price: float = 0.00, purchase_date: str = None) -> dict:
9898
"""Adds a single new position to the the portfolio.
9999
100100
Arguments:
@@ -220,7 +220,7 @@ def in_portfolio(self, symbol: str) -> bool:
220220
221221
Returns:
222222
----
223-
bool -- `True` if the position is in the portfolio, `False` otherwise.
223+
bool -- `True` if the position is in the portfolio, `False` otherwise.
224224
"""
225225

226226
if symbol in self.positions:
@@ -243,8 +243,8 @@ def is_porfitable(self, symbol: str, current_price: float) -> bool:
243243
244244
Returns:
245245
----
246-
bool -- Specifies whether the position is profitable or flat (True) or not
247-
profitable (False).
246+
bool -- Specifies whether the position is profitable or flat (True) or not
247+
profitable (False).
248248
"""
249249

250250
if symbol in self.positions and self.positions[symbol]['purchase_price'] < current_price:

‎pyrobot/robot.py

+89-2
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,19 @@
1+
import pandas as pd
2+
13
from td.client import TDClient
2-
from datetime import datetime, time, timezone
4+
from td.utils import milliseconds_since_epoch
5+
6+
from datetime import datetime
7+
from datetime import time
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from datetime import timezone
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310
from pyrobot.portfolio import Portfolio
411
from pyrobot.trades import Trade
12+
from pyrobot.stock_frame import StockFrame
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14+
from typing import List
15+
from typing import Dict
16+
from typing import Union
517

618
class PyRobot():
719

@@ -29,9 +41,11 @@ def __init__(self, client_id: str, redirect_uri: str, credentials_path: str = No
2941
self.trading_account: str = trading_account
3042
self.client_id: str = client_id
3143
self.redirect_uri: str = redirect_uri
32-
self.credentials_path:str = credentials_path
44+
self.credentials_path: str = credentials_path
3345
self.session: TDClient = self._create_session()
3446
self.trades: dict = {}
47+
self.historical_prices: dict = {}
48+
self.stock_frame = None
3549

3650
def _create_session(self) -> TDClient:
3751
"""Start a new session.
@@ -351,3 +365,76 @@ def grab_current_quotes(self) -> dict:
351365
quotes = self.session.get_quotes(instruments = list(symbols))
352366

353367
return quotes
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def grab_historical_prices(self, start: datetime, end: datetime, bar_size: int = 1, bar_type: str = 'minute', data_frame: bool = False) -> Union[List[Dict], pd.DataFrame]:
370+
"""Grabs the historical prices for all the postions in a portfolio.
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Overview:
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----
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Any of the historical price data returned will include extended hours
375+
price data by default.
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Arguments:
378+
----
379+
start {datetime} -- Defines the start date for the historical prices.
380+
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end {datetime} -- Defines the end date for the historical prices.
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Keyword Arguments:
384+
----
385+
bar_size {int} -- Defines the size of each bar. (default: {1})
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387+
bar_type {str} -- Defines the bar type, can be one of the following:
388+
`['minute', 'week', 'month', 'year']` (default: {'minute'})
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Returns:
391+
----
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{List[Dict]} -- The historical price candles.
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Usage:
395+
----
396+
"""
397+
398+
start = str(milliseconds_since_epoch(dt_object=start))
399+
end = str(milliseconds_since_epoch(dt_object=end))
400+
401+
new_prices = []
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403+
for symbol in self.portfolio.positions:
404+
405+
historical_prices_response = self.session.get_price_history(
406+
symbol=symbol,
407+
period_type='day',
408+
start_date=start,
409+
end_date=end,
410+
frequency_type=bar_type,
411+
frequency=bar_size,
412+
extended_hours=True
413+
)
414+
415+
self.historical_prices[symbol] = {}
416+
self.historical_prices[symbol]['candles'] = historical_prices_response['candles']
417+
418+
for candle in historical_prices_response['candles']:
419+
420+
new_price_mini_dict = {}
421+
new_price_mini_dict['symbol'] = symbol
422+
new_price_mini_dict['open'] = candle['open']
423+
new_price_mini_dict['close'] = candle['close']
424+
new_price_mini_dict['high'] = candle['high']
425+
new_price_mini_dict['low'] = candle['low']
426+
new_price_mini_dict['volume'] = candle['volume']
427+
new_price_mini_dict['datetime'] = candle['datetime']
428+
new_prices.append(new_price_mini_dict)
429+
430+
self.historical_prices['aggregated'] = new_prices
431+
432+
return self.historical_prices
433+
434+
def create_stock_frame(self, data: List[dict]) -> StockFrame:
435+
436+
self.stock_frame = StockFrame(data=data)
437+
438+
return self.stock_frame
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