ft-pandas-ta

Common errors

• ImportError: cannot import name 'NaN' from 'numpy'

  cause The `ft-pandas-ta` library (or underlying `pandas-ta` module) is trying to import `NaN` from `numpy`, but modern NumPy (e.g., 2.0+) uses `nan` (lowercase).
  fix Upgrade `ft-pandas-ta` to 0.3.16 or newer (`pip install --upgrade ft-pandas-ta`). If the problem persists, you might need to downgrade `numpy` to an older version like `numpy==1.26.3` or manually edit the problematic `ft-pandas-ta` source file if a fix isn't available.

• KeyError: 'close'

  cause An indicator function was called on a DataFrame, but the required 'close' column (or other OHLCV columns like 'open', 'high', 'low', 'volume') was not found.
  fix Verify that your DataFrame has columns named 'open', 'high', 'low', 'close', 'volume' (case-insensitive for the `.ta` accessor but explicit calls require exact names). Rename columns if necessary (e.g., `df.columns = df.columns.str.lower()`).

• AttributeError: 'DataFrame' object has no attribute 'ta'

  cause The `pandas_ta` module was not imported, or it was imported in a way that did not activate the DataFrame extension. The `.ta` accessor is added to DataFrames upon importing `pandas_ta`.
  fix Ensure you have `import pandas_ta as ta` (or similar) at the beginning of your script. This import statement activates the `.ta` accessor for all Pandas DataFrames.

• TypeError: Cannot compare types '_____' and '_____' (for example, string and int) when performing operations involving indicator calculations.

  cause Input columns for indicators contain mixed data types or non-numeric types (e.g., strings) where numbers are expected.
  fix Inspect the `dtypes` of your DataFrame columns, especially those used in indicator calculations. Convert relevant columns to numeric types (e.g., `pd.to_numeric(df['column'], errors='coerce')`) and handle any `NaN` values that result from coercion.

Warnings

• breaking Older versions (prior to 0.3.16) might encounter `DeprecationWarning` related to `pkg_resources` due to changes in Python packaging.
  Fix: Upgrade to `ft-pandas-ta` version 0.3.16 or newer: `pip install --upgrade ft-pandas-ta`.
• breaking When upgrading to NumPy 2.0+, older versions of `pandas-ta` (and potentially `ft-pandas-ta` if not on 0.3.16) may raise `ImportError: cannot import name 'NaN' from 'numpy'`. This is due to `numpy.NaN` being deprecated in favor of `numpy.nan`.
  Fix: Upgrade to `ft-pandas-ta` 0.3.16 or newer. If the error persists, consider downgrading NumPy to `numpy==1.26.3` or manually patching the `squeeze_pro.py` file (as detailed for original `pandas-ta`) by changing `from numpy import NaN as npNaN` to `from numpy import nan as npNaN` if your installed `ft-pandas-ta` version still contains this issue.
• gotcha Some indicators require specific DataFrame column names (e.g., 'open', 'high', 'low', 'close', 'volume'). If these columns are missing or incorrectly named, a `KeyError` will occur.
  Fix: Ensure your DataFrame contains the expected OHLCV columns, typically lowercased. Rename columns if necessary (e.g., `df.columns = df.columns.str.lower()`).
• breaking Pandas 3.0 introduced significant changes to copy/view semantics, deprecating `SettingWithCopyWarning` and enforcing explicit modifications. Chained assignments like `df[df['col'] > 0]['new_col'] = value` no longer work reliably. This could affect how you manipulate DataFrames with `ft-pandas-ta` results.
  Fix: Adopt explicit modification patterns, especially using `.loc` for assignment: `df.loc[df['col'] > 0, 'new_col'] = value`. Understand the new Copy-on-Write behavior in Pandas 3.0.

Install

• pip install ft-pandas-ta Latest stable version

Imports

• ta

  import pandas as pd
  import pandas_ta as ta

  Common alias for both standalone functions and to enable the DataFrame accessor.
• DataFrame.ta
  wrong:

  from ft_pandas_ta import ta # Then df.ta.sma()

  correct:

  import pandas as pd
  import pandas_ta as ta
  
  df = pd.DataFrame() # Your DataFrame
  df.ta.sma(length=10, append=True)

  While 'from ft_pandas_ta import ta' might work for standalone functions, 'import pandas_ta as ta' is the canonical way to import the library and automatically enable the DataFrame '.ta' accessor. The 'ft_pandas_ta' package uses 'pandas_ta' as its internal module name.

Quickstart

This quickstart demonstrates fetching historical stock data using `yfinance`, then calculating a Simple Moving Average (SMA), Relative Strength Index (RSI), and Bollinger Bands using `ft-pandas-ta`. It showcases both appending indicators directly to the DataFrame and returning them as a Series.

import pandas as pd
import pandas_ta as ta
import yfinance as yf # Common dependency for fetching financial data

# Fetch sample financial data
df = yf.download('AAPL', start='2022-01-01', end='2023-01-01')

# Ensure the index is a DatetimeIndex (yfinance does this by default)
# For other data sources, you might need:
# df.index = pd.to_datetime(df.index)

# Calculate a Simple Moving Average (SMA) and append to DataFrame
df.ta.sma(length=20, append=True)

# Calculate Relative Strength Index (RSI) and get it as a Series
rsi_series = df.ta.rsi(length=14)

# Calculate Bollinger Bands and append multiple columns to DataFrame
df.ta.bbands(append=True)

print(df.tail())
print(rsi_series.tail())