Overall Statistics Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees \$0.00
```import numpy as np

### <summary>
### Example of a simple class that acts as a manualy updated indicator using pandas to
### calculate the rolling std of percent returns of the close price for each asset.
### </summary>
class BasicOOPAlgorithm(QCAlgorithm):
'''
Example of a simple class that acts as a manualy updated indicator using pandas to
calculate the rolling std of percent returns of the close price for each asset.
'''

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2013,10,1)  #Set Start Date
self.SetEndDate(2013,10,5)   #Set End Date
self.SetCash(100000)          #Set Strategy Cash

self.resolution = Resolution.Daily

self.universe = [
]

# Store per-asset indicators in a dictionary
self.std_of_returns = {}
for symbol in self.universe:
# initialize the object
self.std_of_returns[symbol] = StdOfReturns(self, symbol, 10, self.resolution)

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

Arguments:
data: Slice object keyed by symbol containing the stock data
'''
for symbol in self.universe:

# update the rolling metric with new data
self.std_of_returns[symbol].update(self.Securities[symbol].Price)

self.Log("%s\t:\t%0.3f"%(symbol, self.std_of_returns[symbol].value))

class StdOfReturns():

def __init__(self, algo, symbol, window, resolution):
# set up params of per-asset rolling metric calculation
self.symbol = symbol
self.window = window
self.resolution = resolution

# download the window. Prob not great to drag algo scope in here. Could get outside and pass in.
self.history = algo.History([symbol], window, self.resolution).close.values

# calulate the metrics for the current window
self.compute()

def update(self, value):
# update history, retain length
self.history = np.append(self.history, float(value))[1:]

# calulate the metrics for the current window
self.compute()

def compute(self):
# calc percent returns
r_p = np.diff(self.history)/self.history[:-1]

# calc std of returns for current widow
std_r = np.std(r_p)

# update value
self.value = std_r                        ```