data leakage in oscillator?

[quant2008]

Hello, the oscillator is as follows.
here, s = diff.std(), this std uses full data series, isn't it a leakage of future data in early days?

compute the oscillator

def osc(prices, fast=32, slow=96, scaling=True):
"""
oscillator

Args:
    prices: a dataframe of prices
    fast: fast moving average factor, e.g. 32
    slow: slow moving average factor, e.g. 96
    scaling: true/false. If true scales with the standard deviation of the signal
    Strictly speacking this step is forward looking.

Returns:
    oscillator
"""
diff = prices.ewm(com=fast - 1).mean() - prices.ewm(com=slow - 1).mean()
if scaling:
    s = diff.std()
else:
    s = 1

return diff / s

[tschm]

It is. I think I have documented this before. One could either use a moving std or use the analytical formula.

…

On Tue, 23 May 2023 at 19:04, zhangliang ***@***.***> wrote: Hello, the oscillator is as follows. here, s = diff.std(), this std uses full data series, isn't it a leakage of future data in early days? compute the oscillator def osc(prices, fast=32, slow=96, scaling=True): """ oscillator Args: prices: a dataframe of prices fast: fast moving average factor, e.g. 32 slow: slow moving average factor, e.g. 96 scaling: true/false. If true scales with the standard deviation of the signal Strictly speacking this step is forward looking. Returns: oscillator """ diff = prices.ewm(com=fast - 1).mean() - prices.ewm(com=slow - 1).mean() if scaling: s = diff.std() else: s = 1 return diff / s — Reply to this email directly, view it on GitHub <#46>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAPTQ7423AHBXXGSPSIVE63XHVUB7ANCNFSM6AAAAAAYMVJRAU> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[tschm]

I have added a comment on that.