Self liquidating loan program intj entj dating
(I note this is what Yahoo does in their Adj close column if you grab historic data for a specific year.) I am not sure what to do here.
Is there a version of order_target that is compatible with pipeline?
def can_buy(context, data): latest = data[context.spy].close_price h = history(200,'1d','close_price') avg = h[context.spy].mean() return latest avg # This function is for adding new positions, by iterating through the # eligible stocks in order of momentum, and buying them if we have (anticipate # having) enough cash to do so.
Series(range(0,self.window_length)) log_close = np.log(close) scores = np.empty(len(close.
The ATR of is calculated using closing prices, which were around -60 in the 20 days leading up to the calculation date of 2007-12-05.
This means the algorithm correctly calculates it must purchase 50 shares, worth around 00.
Using get an ATR(20) of around , which makes sense as the share price is around 0, so that's a normal daily move of 4%, which is high, but not ridiculous for late 2007.
Breaking the code at this point: def desired_position_size_in_shares(context, data, sid): account_value = context.account.equity_with_loan target_range = Daily Range Per Stock estimated_atr = context.pool['atr'][sid] return (account_value * target_range) / estimated_atr The estimated_atr is around 2, which is wrong.