Why the calculated SMA is quite different by using Built-in Factor and by talib.SMA()?

Since Q will shutdown the living trading and I have to look for another alternative. Since the wonderful Built-in Factor not available elsewhere, I have to change my code using the Built-in-Factor for SMA calculation to use the talib.SMA. But I found the result are quite different.

Attached is an example. As I calculate the SSO's SMA(150) with talib.SMA() from Jul 01, 2006 to Jul 31, 2006, the values are NAN. I can understand because the SSO's inception day is on Jun 19, 2006.

But as I use the Built-in Factor, the values I get are not NAN. Why?

2
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 59ae4c5bb866a0510ded6985
There was a runtime error.
4 responses

If you will use .bfill() you will get same results.

# SMA indicator

import talib

def initialize(context):
schedule_function(record_SMA, date_rules.every_day(), time_rules.market_close())

def record_SMA(context, data):
etf, period  = symbol('SSO'), 150

price = data.current(etf, 'price')
prices = data.history(etf, 'price', period, '1d').bfill()
SMA_ta = talib.SMA(prices, period)
mavg = prices.mean()

print(SMA_ta[-1],mavg)
record(price = price, mavg_ta = SMA_ta[-1], mavg = mavg)


2006-07-03 09:59 PRINT (71.516933333333327, 71.51693333333333)
2006-07-05 12:59 PRINT (71.529820000000001, 71.52982)
2006-07-06 12:59 PRINT (71.545133333333325, 71.54513333333333)
2006-07-07 12:59 PRINT (71.55353333333332, 71.55353333333332)
2006-07-10 12:59 PRINT (71.563333333333318, 71.56333333333332)
2006-07-11 12:59 PRINT (71.577286666666666, 71.57728666666667)
2006-07-12 12:59 PRINT (71.579553333333322, 71.57955333333332)

Hi,

But:
You use the talib.SAM and mean(). They will have the same values. But have you tried using the Built-in-Factor 'SimpleMovingAverage'? They are somewhat different.

Is it a correct way to use the bfill() here? The inception day of SSO was on Jun 19, 2006. There was no price 150 days before this date. The SMA should be NAN. If not, this means it is not really the SMA(150), right?

Hi @Thomas,

The built-in SimpleMovingAverage factor ignores NaN values when computing the mean. On the other hand, NaN values will cause talib.SMA to return an array filled with NaN values. As you noticed, your algorithm is passing NaN values to talib.SMA through stock_history. To solve this, you can drop NaN values from stock_history by using its .dropna() attribute method.

The above solution will introduce another problem. Talib's SMA function will return an array of NaN values if the timeperiod used is larger than the size of the input array. Since we are dropping NaN values from stock_history, we will need to set timeperiod to be the length of stock_history.

The attached backtest applies both fixes. You will notice in the log output that the values returned by both talib.SMA and SimpleMovingAverage are equal.

3
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 59b05ee7e6216e5504511558
There was a runtime error.
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@Ernesto
    if len(stock_history) < context.sma_period: