Hi!

Thanks for TA! Great job! It seems that i'm getting inaccurate data with ADX, at least data is different compared to cryptowatch and other sources. import krakenex import ta from pykrakenapi import KrakenAPI

api = krakenex.API()
k = KrakenAPI(api)
df, last = k.get_ohlc_data("XXBTZEUR", ascending=True, interval=5)
ohlc = ta.utils.dropna(df)
print(ta.trend.adx(ohlc.high, ohlc.low, ohlc.close))

and output is

...
2018-09-25 17:35:00    22.381099
Name: adx, Length: 720, dtype: float64

while to ADX on website is for same interval/lenght is 12.54

The RSI calculated from Ta library is different from the one that you get from Trading View. I compared the results between Ta library and Talib library and , Talib gives you the same results as in tradingview.

Hi, I am getting this error when using the parameter names 'window_slow', 'window_fast', and 'window_sign'. It works ok if I stop after the "close" parameter, but then I can't play with the window sizes. Is it something I've done wrong?

--> 730 ind_MACD = ta.trend.MACD(close=df["close"], window_slow=26, window_fast=12, window_sign=9)

TypeError: __init__() got an unexpected keyword argument 'window_slow'

Just curious on what sort of requirements or expectations you have regarding the API now and moving forward.

• Limited to return a single Series? Is returning a DataFrame an option for indicators that return multiple series, such as MACD, BBANDs, et al?
• Are the indicators to be decoupled and not rely on others for computation? For instance, MACD relying on SMAs?

Thanks KJ

In your documentation, you have a link to MFI formula explanation and there you can see such text: "Money flow is positive when the typical price rises (buying pressure) and negative when the typical price declines (selling pressure)." So, you need to compare typical prices, not close prices.

df.loc[(df['Close'] > df['Close'].shift(1, fill_value=df['Close'].mean())), 'Up_or_Down'] = 1
df.loc[(df['Close'] < df['Close'].shift(1, fill_value=df['Close'].mean())), 'Up_or_Down'] = 2

Here you need to make a comparison with tp.

for cci calculation, we should use "mean absolute deviation (mad)" not standard deviation. please correct it. unfortunately, pandas rolling doesn't provide mad function. we have to find it by our own.

here I'm posting my code which is used for cci calculation.

pp = (data['CLOSE'] + data['LOW'] + data['HIGH'] )/3
mad = lambda x : np.mean(np.abs(x-np.mean(x)))
data['CCI']= (pp-pp.rolling(period).mean())/(0.015 * pp.rolling(period).apply(mad,True))

First: GREAT LIBRARY!!, @bukosabino! (better than all the others!!)

And because of that I would be happy to find the parabolic sar from you in it. It's very complicated. I will try to code it for myself. If I'm ready with a good solution I will send it to you. But maybe you're faster! ;)

According to https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud for icichimoku cloud the values are shifted forward. Where can I get these values, are they discarded?

Allow users to define custom number of ATRs away from the central moving average

https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels

Adding Scheff Trend Cycle indicator Manually copy data from tradeview charts. Testing data round to 2 decimal places

Adding Stoch RSI indicator, percentage volume oscillator, percentage price oscillator, ulcer index

From: https://medium.com/@neonguru/it-would-be-nice-if-it-included-vwap-22fa4e80584f

It would be nice if it included VWAP.

More info: https://school.stockcharts.com/doku.php?id=technical_indicators:vwap_intraday

I fixed this indicator. It was completely wrong. Important things:

1. Now it returns Series with NaN values by default unless new optional method parameter dropnans is not True OR you can specify self._check_fillna(self._vpt, value=-1) instead of value=0 to make it bfill, but I would not recommend it. Therefore it returns self._vpt with property self._vpt.shape[0] <= self.close.shape[0].
2. Provided new optional method parameter smoothing_factor like in TradingView implementation of this indicator. But it's not used by default (like in pure definition of this indicator).
3. Added some type|value checking, feel free to delete it. I added them to feel safe and didn't find a place with checkers.
4. I didnt'find a place to add important comment about shifting and fill_value with series mean, so I did it in 273 line... Not in right place but ... whatever. It should be deleted, I guess.

trend.py:730: FutureWarning: The behavior of series[i:j] with an integer-dtype index is deprecated. In a future version, this will be treated as label-based indexing, consistent with e.g. series[i] lookups. To retain the old behavior, use series.iloc[i:j]. (...)

Same thing on line 748 and 760. It's not really important at the moment but it's a quick fix (just add iloc after dropna), and because I don't like ignoring warnings I already have the pull request ready :)

This is not the standard calculation. It does not match the bb calculated from any other charting software I use, and the bands are now biased. What is the reasoning here?

When coming across an error, I would be able to just remove the feature that was causing the error and process the rest.

df_window = add_all_ta_features( df_window, open="open", high="high", low="low", close="close", volume="volume", )

Would be something like this:

df_window = add_all_ta_features( df_window, open="open", high="high", low="low", close="close", volume="volume", exclude_ta_features=["trend_dpo", "momentum_stoch_rsi"] )

I'm getting this error below, so I would simply remove the feature that is causing it. In my case is probably indicator_adx or add_trend_ta is the feature probably causing it. That's why I think a parameter to pick which feature to process would be helpful.

Traceback (most recent call last):

  File "C:\Users\artur\AppData\Local\Temp\ipykernel_7992\260366147.py", line 1, in <cell line: 1>
    df_window_pandas = add_all_ta_features(

  File "C:\Users\artur\miniforge3\envs\env_tsflex\lib\site-packages\ta\wrapper.py", line 585, in add_all_ta_features
    df = add_trend_ta(

  File "C:\Users\artur\miniforge3\envs\env_tsflex\lib\site-packages\ta\wrapper.py", line 343, in add_trend_ta
    df[f"{colprefix}trend_adx"] = indicator_adx.adx()

  File "C:\Users\artur\miniforge3\envs\env_tsflex\lib\site-packages\ta\trend.py", line 790, in adx
    adx_series[self._window] = directional_index[0: self._window].mean()

IndexError: index 14 is out of bounds for axis 0 with size 9