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pep8 conversion of python algos (#7948)

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* pep8 conversion of python algos

* adding 10 more pep8 converted algos

* PEP8 updates/fixes

---------

Co-authored-by: Jhonathan Abreu <jdabreu25@gmail.com>
This commit is contained in:
Ashutosh
2024-04-19 19:36:48 +05:30
committed by GitHub
parent 77591f90c7
commit 3c30e255fe
21 changed files with 490 additions and 491 deletions
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Algorithm.Python/TensorFlowNeuralNetworkAlgorithm.py
+22 -22
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@@ -16,34 +16,34 @@ import tensorflow.compat.v1 as tf
class TensorFlowNeuralNetworkAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2013, 10, 7) # Set Start Date
self.SetEndDate(2013, 10, 8) # Set End Date
def initialize(self):
self.set_start_date(2013, 10, 7) # Set Start Date
self.set_end_date(2013, 10, 8) # Set End Date
self.SetCash(100000) # Set Strategy Cash
spy = self.AddEquity("SPY", Resolution.Minute) # Add Equity
self.set_cash(100000) # Set Strategy Cash
spy = self.add_equity("SPY", Resolution.MINUTE) # Add Equity
self.symbols = [spy.Symbol] # potential trading symbols pool (in this algorithm there is only 1).
self.symbols = [spy.symbol] # potential trading symbols pool (in this algorithm there is only 1).
self.lookback = 30 # number of previous days for training
self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), self.TimeRules.AfterMarketOpen("SPY", 28), self.NetTrain) # train the neural network 28 mins after market open
self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), self.TimeRules.AfterMarketOpen("SPY", 30), self.Trade) # trade 30 mins after market open
self.schedule.on(self.date_rules.every(DayOfWeek.MONDAY), self.time_rules.after_market_open("SPY", 28), self.net_train) # train the neural network 28 mins after market open
self.schedule.on(self.date_rules.every(DayOfWeek.MONDAY), self.time_rules.after_market_open("SPY", 30), self.trade) # trade 30 mins after market open
def add_layer(self, inputs, in_size, out_size, activation_function=None):
# add one more layer and return the output of this layer
# this is one NN with only one hidden layer
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
wx_plus_b = tf.matmul(inputs, weights) + biases
if activation_function is None:
outputs = Wx_plus_b
outputs = wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
outputs = activation_function(wx_plus_b)
return outputs
def NetTrain(self):
def net_train(self):
# Daily historical data is used to train the machine learning model
history = self.History(self.symbols, self.lookback + 1, Resolution.Daily)
history = self.history(self.symbols, self.lookback + 1, Resolution.DAILY)
# model: use prices_x to fit prices_y; key: symbol; value: according price
self.prices_x, self.prices_y = {}, {}
@@ -55,8 +55,8 @@ class TensorFlowNeuralNetworkAlgorithm(QCAlgorithm):
if not history.empty:
# Daily historical data is used to train the machine learning model
# use open prices to predict the next days'
self.prices_x[symbol] = list(history.loc[symbol.Value]['open'][:-1])
self.prices_y[symbol] = list(history.loc[symbol.Value]['open'][1:])
self.prices_x[symbol] = list(history.loc[symbol.value]['open'][:-1])
self.prices_y[symbol] = list(history.loc[symbol.value]['open'][1:])
for symbol in self.symbols:
if symbol in self.prices_x:
@@ -97,14 +97,14 @@ class TensorFlowNeuralNetworkAlgorithm(QCAlgorithm):
self.sell_prices[symbol] = y_pred_final - np.std(y_data)
self.buy_prices[symbol] = y_pred_final + np.std(y_data)
def Trade(self):
def trade(self):
'''
Enter or exit positions based on relationship of the open price of the current bar and the prices defined by the machine learning model.
Liquidate if the open price is below the sell price and buy if the open price is above the buy price
'''
for holding in self.Portfolio.Values:
if self.CurrentSlice[holding.Symbol].Open < self.sell_prices[holding.Symbol] and holding.Invested:
self.Liquidate(holding.Symbol)
for holding in self.portfolio.Values:
if self.current_slice[holding.symbol].open < self.sell_prices[holding.symbol] and holding.invested:
self.liquidate(holding.symbol)
if self.CurrentSlice[holding.Symbol].Open > self.buy_prices[holding.Symbol] and not holding.Invested:
self.SetHoldings(holding.Symbol, 1 / len(self.symbols))
if self.current_slice[holding.symbol].open > self.buy_prices[holding.symbol] and not holding.invested:
self.set_holdings(holding.symbol, 1 / len(self.symbols))