TA - all features generations about pandas-ta HOT 11 CLOSED

Hello @similang,

Which version of Pandas TA are you using?

The KeyError: 'high' means that 'high' was not found in your DataFrame df. Are you sure you did not mean: df1.ta.strategy(name='all') in your function: ta_pd_v2(df)?

Hope this helps!
KJ

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Version: 0.1.66b0

Ah, my bad - I didn't put my 3rd customised function. Not use to GitHub comment sections. Perhaps it will be easier to diagnose with data.

Here you go:

ch.zip

from pandas-ta.

Hello @similang,

Give this a shot.

import pandas as pd
import pandas_ta as ta

if __name__ == "__main__":
    df = pd.read_csv("ch.csv") # Or wherever your csv is located
    df["open-1"] = df["open"].shift(1)
    df["high-1"] = df["high"].shift(1)
    df["low-1"] = df["low"].shift(1)
    df["close-1"] = df["close"].shift(1)

    df.ta.strategy(name='all', verbose=True) # appends to df by default
    print(df.tail())
    print(', '.join(list(df.columns))) # For verification of columns added

The output I got from above:

Trend Return

As a side note, trend_return is a special function and is naturally excluded from all
since it requires trend logic, see the last example for a comprehensive version.

In short, suppose you wanted to calculate the Cumulative Log Returns of the trend EMA 10 > SMA 50. Here are two different ways using TA Lib vs Pandas DataFrame Style (whichever is more logical for you):

import pandas as pd
import pandas_ta as ta

def tr_talib_style(df, cumulative):
    # TA Lib Style
    closedf = df['close']
    long = ta.ema(closedf, 10) > ta.sma(closedf, 50)
    return ta.trend_return(closedf, long, cumulative=cumulative)

def tr_dataframe_style(df, cumulative):
    # DataFrame Style
    long = df.ta.ema(length=10) > df.ta.sma(length=50)
    df.ta.trend_return(trend=long, cumulative=cumulative, append=True)
    return df

if __name__ == "__main__":
    cumulative = True
    df = pd.read_csv("ch.csv") # Or wherever your csv is located
    df['CumLogTR'] = tr_talib_style(df, cumulative=cumulative)
    df = tr_dataframe_style(df, cumulative=cumulative)
    print(df.tail(10))

Hope this helps! Let me know how it works out.

Thanks,
KJ

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Hello @similang,

I assume by no response that the response provided was sufficient. Thus I will be closing this issue in a few days.

Don't forget to ⭐ if you find the library useful.

Thanks,
KJ

from pandas-ta.

Thanks. Will give it a try first and respond accordingly when I am slightly more free.

from pandas-ta.

Hi, just wondering - the 2nd part - tr_talib_style, tr_dataframe_style i hit error, i.e: in column 'close'; not sure why...

Also, how to select a subset of all strategy columns?

Thanks.

from pandas-ta.

Hey,

the 2nd part - tr_talib_style, tr_dataframe_style i hit error, i.e: in column 'close'; not sure why...

I do not know either. 🤷‍♂️ You have given little for me to diagnose. Is there a 'close' column in the DataFrame? Are the open, high, low, close, volume all lowercase as mentioned in the README? Your 'volume' column is misspelled in the ch.csv, but that wouldn't throw as 'close' column error in the simple trend_return example.

Here are my results from the 2nd part:

Also, how to select a subset of all strategy columns?

What do you mean? Just the momentum indicators or the overlap indicators et al? Or do you mean, you want to select certain columns after df.ta.strategy() has completed?

from pandas-ta.

To be exact on part 1 - gotta rename 'volumn' to 'volume' for the script to run.

Oh ya, I meant selecting certain columns for df.ta.strategy() => if i do not want all variables be populated since I am doing mass processing on multiple indices/equities.

from pandas-ta.

To be exact on part 1 - gotta rename 'volumn' to 'volume' for the script to run.

👍

Oh ya, I meant selecting certain columns for df.ta.strategy() => if i do not want all variables be populated since I am doing mass processing on multiple indices/equities.

Of course. That is something I have been trying to finish for the next release: Custom Strategies and Strategy Composition/Chaining. Because running df.ta.strategy(), which defaults to "All" and appending currently 152 new columns is both unwise and inefficient imo even if multiprocessing enabled.

Here are some ways to target the columns. The last one is specific but requires knowing the name of the columns.

import pandas as pd
import pandas_ta as ta

if __name__ == "__main__":
    _df = pd.read_csv("data/similang-ch.csv")
    df = _df.copy()
    df.ta.strategy(verbose=True)

    cols_as_list = list(df.columns)
    cols_as_string = ', '.join(cols_as_list)
    # print("DataFrame Columns Property (hard to figure which to use):\n", df.columns)
    # print("\n", "DataFrame Columns Names (a bit easier to find):\n", cols_as_list)
    print("\n", "DataFrame Columns Names (way easier to find):\n", cols_as_string)

    print("\n", "If you want say, the last ten columns:\n", df[df.columns[-10:]])
    print("\n", "Or maybe the Column Indicies from 5 to 20:\n", df[df.columns[5:20]])

    print("\n", "If you know what you want, say MACD and RSI:")
    print(df[["RSI_14", "MACD_12_26_9", "MACDh_12_26_9", "MACDs_12_26_9"]])

Custom Strategies

Almost finished!

import pandas as pd
import pandas_ta as ta

if __name__ == "__main__":
    _df = pd.read_csv("data/similang-ch.csv")

    df = _df.copy()
    df.ta.strategy(ta.CommonStrategy, verbose=True)
    print("\nSoon a Builtin Common Strategy:", ta.CommonStrategy.name)
    print(ta.CommonStrategy, "\n")
    print(df)

    df = _df.copy()
    momo_bands_sma_ta = [
        {"kind":"sma", "length": 50},
        {"kind":"sma", "length": 200},
        {"kind":"bbands", "length": 20},
        {"kind":"macd"},
        {"kind":"rsi"},
        {"kind":"log_return", "cumulative": True},
        {"kind":"sma", "close": "CUMLOGRET_1", "length": 5, "suffix": "CUMLOGRET"},
    ]
    momo_bands_sma_strategy = ta.Strategy(
        "Momo, Bands and SMAs and Cumulative Log Returns", # name
        momo_bands_sma_ta, # ta
        "MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns" # description
    )
    print("\nOr your own Custom Strategy:", momo_bands_sma_strategy.name)
    print(momo_bands_sma_strategy, "\n")
    df.ta.strategy(momo_bands_sma_strategy, timed=True)
    print(df)

Yielding:

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@similang

Check out the latest version!

$ pip install -U git+https://github.com/twopirllc/pandas-ta

I assume by no response that the response provided was sufficient!?

Regards

from pandas-ta.

Nice - let me check it up. Thanks.

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