Add pandas DataFrame extension accessor (.ta) for technical indicators #20

Copilot · 2025-11-30T22:46:06Z

Implements a pandas DataFrame accessor mirroring pandas-ta's API, enabling direct calculation and appending of technical indicators to DataFrames.

Changes

src/numta/pandas_ext.py - New TAAccessor class with:
- Case-insensitive OHLCV column auto-detection (open, Open, o, etc.)
- append=True/False parameter to append columns or return Series/DataFrame
- All 136+ indicators as methods (overlap, momentum, volume, volatility, cycle, statistics, math, price transforms, 60+ candlestick patterns)
- TA-Lib/pandas-ta column naming conventions (SMA_20, MACD_12_26_9, BBU_20_2.0)
src/numta/__init__.py - Auto-registers accessor on import
pyproject.toml - Added pandas optional dependency
tests/test_pandas_ext.py - 38 tests covering all indicator categories
README.md - Pandas Integration documentation section

Usage

import pandas as pd
import numta

df = pd.read_csv("prices.csv")

# Return Series
sma = df.ta.sma(timeperiod=20)

# Append to DataFrame
df.ta.sma(timeperiod=20, append=True)   # Adds 'SMA_20'
df.ta.macd(append=True)                  # Adds 'MACD_12_26_9', 'MACDSignal_12_26_9', 'MACDHist_12_26_9'
df.ta.cdldoji(append=True)               # Adds 'CDLDOJI'

# Custom column
df.ta.sma(column='adj_close', timeperiod=20, append=True)

Install with: pip install "numta[pandas]"

Original prompt

Overview

Implement a pandas DataFrame extension accessor (.ta) to mirror the functionality found in pandas-ta. This allows seamless calculation and appending of technical indicators directly to a Pandas DataFrame, ensuring a modern, frictionless user experience for algorithmic trading workflows.

Motivation

Modernizes user experience—no need for manual array transformations.
Direct integration into pandas for data scientists and practitioners.
Reduces friction for users migrating from pandas-ta (which is abandoned since 2019).

Current Codebase Context

Project Structure

numta/
├── src/
│   └── numta/
│       ├── __init__.py           # Main package exports (all indicators listed here)
│       ├── backend.py            # Backend selection logic
│       ├── benchmark.py          # Performance measurement tools
│       ├── optimized.py          # Optimized implementations
│       ├── api/                   # Public API layer
│       │   ├── overlap.py        # Overlap studies (SMA, EMA, BBANDS, DEMA, TEMA, TRIMA, WMA, KAMA, MAMA, T3, SAR, SAREXT)
│       │   ├── momentum_indicators.py  # ADX, ADXR, APO, AROON, AROONOSC, ATR, BOP, CCI, CMO, DX, MACD, MACDEXT, MACDFIX, MFI, MINUS_DI, MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROC, ROCP, ROCR, ROCR100, RSI, STOCH, STOCHF, STOCHRSI, TRIX, ULTOSC, WILLR
│       │   ├── volume_indicators.py    # AD, OBV, ADOSC
│       │   ├── volatility_indicators.py # NATR, TRANGE
│       │   ├── cycle_indicators.py     # HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE
│       │   ├── statistic_functions.py  # BETA, CORREL, LINEARREG, LINEARREG_ANGLE, LINEARREG_INTERCEPT, LINEARREG_SLOPE
│       │   ├── statistics.py           # STDDEV, TSF, VAR
│       │   ├── math_operators.py       # MAX, MAXINDEX, MIN, MININDEX, MINMAX, MINMAXINDEX, SUM
│       │   ├── price_transform.py      # MEDPRICE, MIDPOINT, MIDPRICE, TYPPRICE, WCLPRICE
│       │   └── pattern_recognition.py  # 60+ candlestick patterns (CDL2CROWS, CDL3BLACKCROWS, CDLDOJI, CDLENGULFING, CDLHAMMER, CDLMARUBOZU, etc.)
│       └── cpu/                   # CPU/Numba implementations
├── tests/                         # Test suite
├── pyproject.toml
└── README.md

Current Indicator Signatures (from src/numta/api/overlap.py)

def SMA(close: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
def EMA(close: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
def BBANDS(close: Union[np.ndarray, list], timeperiod: int = 5, nbdevup: float = 2.0, nbdevdn: float = 2.0, matype: int = 0) -> tuple:
def DEMA(close: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
def KAMA(close: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
def MA(close: Union[np.ndarray, list], timeperiod: int = 30, matype: int = 0) -> np.ndarray:
def MAMA(close: Union[np.ndarray, list], fastlimit: float = 0.5, slowlimit: float = 0.05) -> tuple:
def SAR(high: Union[np.ndarray, list], low: Union[np.ndarray, list], acceleration: float = 0.02, maximum: float = 0.2) -> np.ndarray:
def SAREXT(high: Union[np.ndarray, list], low: Union[np.ndarray, list], ...) -> np.ndarray:
def TEMA(data: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
def T3(data: Union[np.ndarray, list], timeperiod: int = 5, vfactor: float = 0.7) -> np.ndarray:
def TRIMA(data: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
def WMA(data: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:

All Available Indicators (from src/numta/init.py)

# Overlap Studies
SMA, EMA, DEMA, BBANDS, KAMA, MA, MAMA, SAR, SAREXT, T3, TEMA, TRIMA, WMA

# Momentum Indicators
ADX, ADXR, APO, AROON, AROONOSC, ATR, BOP, CCI, CMO, DX, MACD, MACDEXT, MACDFIX, MFI, MINUS_DI, MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROC, ROCP, ROCR, ROCR100, RSI, STOCH, STOCHF, STOCHRSI, TRIX, ULTOSC, WILLR

# Volume Indicators
AD, OBV, ADOSC

# Volatility Indicators  
NATR, TRANGE

# Cycle Indicators
HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE

# Statistic Functions
BETA, CORREL, LINEARREG, LINEARREG_ANGLE, LINEARREG_INTERCEPT, LINEARREG_SLOPE, STDDEV, TSF, VAR

# Math Operators
MAX, MAXINDEX, MIN, MININDEX, MINMAX, MINMAXINDEX, SUM

# Price Transform
MEDPRICE, MIDPOINT, MIDPRICE, TYPPRICE, WCLPRICE

# Pattern Recognition (60+ patterns)
CDL2CROWS, CDL3BLACKCROWS, CDL3INSIDE, CDL3OUTSIDE, CDL3STARSINSOUTH, CDL3WHITESOLDIERS,
CDLABANDONEDBABY, CDLADVANCEBLOCK, CDLBELTHOLD, CDLBREAKAWAY, CDLCLOSINGMARUBOZU, 
CDLCONCEALBABYSWALL, CDLCOUNTERATTACK, CDLDARKCLOUDCOVER, CDLDOJI, CDLDOJISTAR,
CDLDRAGONFLYDOJI, CDLENGULFING, CDLEVENINGDOJISTAR, CDLEVENINGSTAR, CDLGAPSIDESIDEWHITE, 
CDLGRAVESTONEDOJI, CDLHAMMER, CDLHANGINGMAN, CDLHARAMI, CDLHARAMICROSS, CDLHIGHWAVE,
CDLHIKKAKE, CDLHIKKAKEMOD, CDLHOMINGPIGEON, CDLIDENTICAL3CROWS, CDLINNECK,
CDLINVERTEDHAMMER, CDLKICKING, CDLKICKINGBYLENGTH, CDLLADDERBOTTOM, CDLLONGLEGGEDDOJI, 
CDLLONGLINE, CDLMARUBOZU, CDLMATCHINGLOW,...

</details>

*This pull request was created as a result of the following prompt from Copilot chat.*
> ## Overview
> Implement a pandas DataFrame extension accessor (`.ta`) to mirror the functionality found in pandas-ta. This allows seamless calculation and appending of technical indicators directly to a Pandas DataFrame, ensuring a modern, frictionless user experience for algorithmic trading workflows.
> 
> ---
> 
> ## Motivation
> - Modernizes user experience—no need for manual array transformations.
> - Direct integration into pandas for data scientists and practitioners.
> - Reduces friction for users migrating from pandas-ta (which is abandoned since 2019).
> 
> ## Current Codebase Context
> 
> ### Project Structure
> ```
> numta/
> ├── src/
> │   └── numta/
> │       ├── __init__.py           # Main package exports (all indicators listed here)
> │       ├── backend.py            # Backend selection logic
> │       ├── benchmark.py          # Performance measurement tools
> │       ├── optimized.py          # Optimized implementations
> │       ├── api/                   # Public API layer
> │       │   ├── overlap.py        # Overlap studies (SMA, EMA, BBANDS, DEMA, TEMA, TRIMA, WMA, KAMA, MAMA, T3, SAR, SAREXT)
> │       │   ├── momentum_indicators.py  # ADX, ADXR, APO, AROON, AROONOSC, ATR, BOP, CCI, CMO, DX, MACD, MACDEXT, MACDFIX, MFI, MINUS_DI, MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROC, ROCP, ROCR, ROCR100, RSI, STOCH, STOCHF, STOCHRSI, TRIX, ULTOSC, WILLR
> │       │   ├── volume_indicators.py    # AD, OBV, ADOSC
> │       │   ├── volatility_indicators.py # NATR, TRANGE
> │       │   ├── cycle_indicators.py     # HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE
> │       │   ├── statistic_functions.py  # BETA, CORREL, LINEARREG, LINEARREG_ANGLE, LINEARREG_INTERCEPT, LINEARREG_SLOPE
> │       │   ├── statistics.py           # STDDEV, TSF, VAR
> │       │   ├── math_operators.py       # MAX, MAXINDEX, MIN, MININDEX, MINMAX, MINMAXINDEX, SUM
> │       │   ├── price_transform.py      # MEDPRICE, MIDPOINT, MIDPRICE, TYPPRICE, WCLPRICE
> │       │   └── pattern_recognition.py  # 60+ candlestick patterns (CDL2CROWS, CDL3BLACKCROWS, CDLDOJI, CDLENGULFING, CDLHAMMER, CDLMARUBOZU, etc.)
> │       └── cpu/                   # CPU/Numba implementations
> ├── tests/                         # Test suite
> ├── pyproject.toml
> └── README.md
> ```
> 
> ### Current Indicator Signatures (from src/numta/api/overlap.py)
> ```python
> def SMA(close: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
> def EMA(close: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
> def BBANDS(close: Union[np.ndarray, list], timeperiod: int = 5, nbdevup: float = 2.0, nbdevdn: float = 2.0, matype: int = 0) -> tuple:
> def DEMA(close: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
> def KAMA(close: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
> def MA(close: Union[np.ndarray, list], timeperiod: int = 30, matype: int = 0) -> np.ndarray:
> def MAMA(close: Union[np.ndarray, list], fastlimit: float = 0.5, slowlimit: float = 0.05) -> tuple:
> def SAR(high: Union[np.ndarray, list], low: Union[np.ndarray, list], acceleration: float = 0.02, maximum: float = 0.2) -> np.ndarray:
> def SAREXT(high: Union[np.ndarray, list], low: Union[np.ndarray, list], ...) -> np.ndarray:
> def TEMA(data: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
> def T3(data: Union[np.ndarray, list], timeperiod: int = 5, vfactor: float = 0.7) -> np.ndarray:
> def TRIMA(data: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
> def WMA(data: Union[np.ndarray, list], timeperiod: int = 30) -> np.ndarray:
> ```
> 
> ### All Available Indicators (from src/numta/__init__.py)
> ```python
> # Overlap Studies
> SMA, EMA, DEMA, BBANDS, KAMA, MA, MAMA, SAR, SAREXT, T3, TEMA, TRIMA, WMA
> 
> # Momentum Indicators
> ADX, ADXR, APO, AROON, AROONOSC, ATR, BOP, CCI, CMO, DX, MACD, MACDEXT, MACDFIX, MFI, MINUS_DI, MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROC, ROCP, ROCR, ROCR100, RSI, STOCH, STOCHF, STOCHRSI, TRIX, ULTOSC, WILLR
> 
> # Volume Indicators
> AD, OBV, ADOSC
> 
> # Volatility Indicators  
> NATR, TRANGE
> 
> # Cycle Indicators
> HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE
> 
> # Statistic Functions
> BETA, CORREL, LINEARREG, LINEARREG_ANGLE, LINEARREG_INTERCEPT, LINEARREG_SLOPE, STDDEV, TSF, VAR
> 
> # Math Operators
> MAX, MAXINDEX, MIN, MININDEX, MINMAX, MINMAXINDEX, SUM
> 
> # Price Transform
> MEDPRICE, MIDPOINT, MIDPRICE, TYPPRICE, WCLPRICE
> 
> # Pattern Recognition (60+ patterns)
> CDL2CROWS, CDL3BLACKCROWS, CDL3INSIDE, CDL3OUTSIDE, CDL3STARSINSOUTH, CDL3WHITESOLDIERS,
> CDLABANDONEDBABY, CDLADVANCEBLOCK, CDLBELTHOLD, CDLBREAKAWAY, CDLCLOSINGMARUBOZU, 
> CDLCONCEALBABYSWALL, CDLCOUNTERATTACK, CDLDARKCLOUDCOVER, CDLDOJI, CDLDOJISTAR,
> CDLDRAGONFLYDOJI, CDLENGULFING, CDLEVENINGDOJISTAR, CDLEVENINGSTAR, CDLGAPSIDESIDEWHITE, 
> CDLGRAVESTONEDOJI, CDLHAMMER, CDLHANGINGMAN, CDLHARAMI, CDLHARAMICROSS, CDLHIGHWAVE,
> CDLHIKKAKE, CDLHIKKAKEMOD, CDLHOMINGPIGEON, CDLIDENTICAL3CROWS, CDLINNECK,
> CDLINVERTEDHAMMER, CDLKICKING, CDLKICKINGBYLENGTH, CDLLADDERBOTTOM, CDLLONGLEGGEDDOJI, 
> CDLLONGLINE, CDLMARUBOZU, CDLMATCHINGLOW, CDLMATHOLD, CDLMORNINGDOJISTAR, CDLMORNINGSTAR, 
> CDLONNECK, CDLPIERCING, CDLRICKSHAWMAN, CDLRISEFALL3METHODS, CDLSEPARATINGLINES, 
> CDLSHOOTINGSTAR, CDLSHORTLINE, CDLSPINNINGTOP, CDLSTALLEDPATTERN, CDLSTICKSANDWICH, 
> CDLTAKURI, CDLTASUKIGAP, CDLTHRUSTING, CDLTRISTAR, CDLUNIQUE3RIVER, CDLUPSIDEGAP2CROWS, 
> CDLXSIDEGAP3METHODS
> ```
> 
> ---
> 
> ## Specifications & Deliverables
> 
> ### 1. Create New Module: `src/numta/pandas_ext.py`
> 
> Create a pandas DataFrame extension accessor using `@pd.api.extensions.register_dataframe_accessor("ta")`.
> 
> ### 2. Extension API Design
> 
> ```python
> import pandas as pd
> import numta  # This should auto-register the .ta accessor
> 
> df = pd.read_csv("prices.csv")  # DataFrame with columns: open, high, low, close, volume
> 
> # Calculate and return Series (default behavior)
> sma_series = df.ta.sma(timeperiod=20)
> 
> # Calculate and append to DataFrame
> df.ta.sma(timeperiod=20, append=True)  # Adds column 'SMA_20'
> 
> # Multiple indicators
> df.ta.ema(timeperiod=12, append=True)   # Adds 'EMA_12'
> df.ta.macd(append=True)                  # Adds 'MACD_12_26_9', 'MACDSignal_12_26_9', 'MACDHist_12_26_9'
> df.ta.bbands(timeperiod=20, append=True) # Adds 'BBU_20_2.0', 'BBM_20', 'BBL_20_2.0'
> df.ta.rsi(timeperiod=14, append=True)    # Adds 'RSI_14'
> 
> # Candlestick patterns
> df.ta.cdldoji(append=True)               # Adds 'CDLDOJI'
> df.ta.cdlengulfing(append=True)          # Adds 'CDLENGULFING'
> ```
> 
> ### 3. Column Naming Convention
> 
> Follow TA-Lib and pandas-ta conventions:
> - Single output: `{INDICATOR}_{param1}_{param2}` e.g., `SMA_20`, `EMA_12`, `RSI_14`
> - Multiple outputs (BBANDS): `BBU_{period}_{stddev}`, `BBM_{period}`, `BBL_{period}_{stddev}`
> - Multiple outputs (MACD): `MACD_{fast}_{slow}_{signal}`, `MACDSignal_{fast}_{slow}_{signal}`, `MACDHist_{fast}_{slow}_{signal}`
> - Pattern recognition: `{PATTERN_NAME}` e.g., `CDLDOJI`, `CDLENGULFING`
> 
> ### 4. Auto-detect OHLCV Columns
> 
> The accessor should automatically detect standard column names (case-insensitive):
> - `open`, `Open`, `OPEN`, `o`
> - `high`, `High`, `HIGH`, `h`
> - `low`, `Low`, `LOW`, `l`
> - `close`, `Close`, `CLOSE`, `c`
> - `volume`, `Volume`, `VOLUME`, `v`
> 
> If columns cannot be auto-detected, allow explicit specification:
> ```python
> df.ta.sma(column='adj_close', timeperiod=20, append=True)
> ```
> 
> ### 5. Implementation Requirements
> 
> #### TAAccessor Class Structure
> ```python
> @pd.api.extensions.register_dataframe_accessor("ta")
> class TAAccessor:
>     def __init__(self, pandas_obj: pd.DataFrame):
>         self._obj = pandas_obj
>         self._validate()
>         self._detect_ohlcv_columns()
>     
>     def _validate(self):
>         # Ensure it's a valid DataFrame
>         pass
>     
>     def _detect_ohlcv_columns(self):
>         # Auto-detect open, high, low, close, volume columns
>         pass
>     
>     def _get_column(self, name: str, column: Optional[str] = None) -> np.ndarray:
>         # Get column data as numpy array
>         pass
>     
>     def _append_or_return(self, result, column_names, append: bool):
>         # Either append to DataFrame or return Series/DataFrame
>         pass
>     
>     # Overlap Studies
>     def sma(self, timeperiod: int = 30, column: str = None, append: bool = False): ...
>     def ema(self, timeperiod: int = 30, column: str = None, append: bool = False): ...
>     def bbands(self, timeperiod: int = 5, nbdevup: float = 2.0, nbdevdn: float = 2.0, matype: int = 0, column: str = None, append: bool = False): ...
>     # ... etc for all indicators
>     
>     # Pattern Recognition
>     def cdldoji(self, append: bool = False): ...
>     def cdlengulfing(self, append: bool = False): ...
>     # ... etc for all 60+ patterns
> ```
> 
> ### 6. Update `src/numta/__init__.py`
> 
> Add import to auto-register the accessor:
> ```python
> # At the end of __init__.py
> # Register pandas DataFrame extension
> from . import pandas_ext  # This registers the .ta accessor
> ```
> 
> ### 7. Add pandas as Optional Dependency
> 
> Update `pyproject.toml`:
> ```toml
> [project.optional-dependencies]
> pandas = [
>     "pandas>=1.3.0",
> ]
> # Update dev to include pandas
> dev = [
>     "pytest>=7.0.0",
>     "pytest-benchmark>=4.0.0",
>     "pandas>=1.3.0",
> ]
> ```
> 
> ### 8. Tests
> 
> Create `tests/test_pandas_ext.py` with comprehensive tests:
> - Test all indicator methods work correctly
> - Test `append=True` behavior
> - Test column naming conventions
> - Test auto-detection of OHLCV columns
> - Test custom column specification
> - Test edge cases (empty DataFrame, missing columns, etc.)
> - Compare results with direct numta function calls
> 
> ### 9. Documentation
> 
> Update `README.md` with:
> - New "Pandas Integration" section
> - Usage examples
> - Column naming conventions
> - Migration guide from pandas-ta
> 
> ---
> 
> ## Acceptance Criteria
> 
> 1. **All indicators accessible via `.ta` accessor** - Every indicator in numta (136 total) should be callable via the DataFrame accessor
> 2. **Correct results** - Results must match direct numta function calls exactly
> 3. **Column naming** - Follow TA-Lib/pandas-ta conventions for automatic column naming
> 4. **append parameter** - `append=True` appends to DataFrame, `append=False` (default) returns Series/DataFrame
> 5. **Auto-detection** - Automatically detect OHLCV columns with case-insensitive matching
> 6. **Custom columns** - Allow explicit column specification via `column=` parameter
> 7. **Tests passing** - Comprehensive test coverage for all accessor methods
> 8. **Documentation** - README updated with usage examples
> 
> ---
> 
> ## References
> - [pandas-ta DataFrame accessor pattern](https://github.com/aarigs/pandas-ta)
> - [Pandas DataFrame extension guide](https://pandas.pydata.org/docs/dev/reference/api/pandas.api.extensions.register_dataframe_accessor.html)
> - [TA-Lib API docs](https://ta-lib.org/doc_api/index.html)
> - numta indicator lists in `FUNCTION_IMPLEMENTATIONS.md` and `src/numta/__init__.py`
> 

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---

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Co-authored-by: deepentropy <8287111+deepentropy@users.noreply.github.com>

…t pandas installation Co-authored-by: deepentropy <8287111+deepentropy@users.noreply.github.com>

chatgpt-codex-connector · 2025-11-30T23:22:29Z

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Copilot

Pull request overview

This pull request implements a pandas DataFrame extension accessor (.ta) that provides seamless integration for calculating and appending 136+ technical indicators directly to DataFrames, mirroring the pandas-ta API pattern.

Key Changes:

New pandas accessor enables df.ta.sma() style indicator calculations
Automatic case-insensitive OHLCV column detection supporting multiple naming conventions
Flexible append=True/False parameter to either modify DataFrames in-place or return Series/DataFrame results

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
`src/numta/pandas_ext.py`	Implements TAAccessor class with 136+ indicator methods, column auto-detection, and TA-Lib-compatible naming conventions
`src/numta/__init__.py`	Auto-registers the `.ta` accessor by importing pandas_ext module
`pyproject.toml`	Adds pandas as optional dependency under `[pandas]` extra and creates `[all]` extra group
`tests/test_pandas_ext.py`	Comprehensive test suite covering all indicator categories, edge cases, and API behavior
`README.md`	Adds "Pandas Integration" documentation section with usage examples and migration guide

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Copilot · 2025-11-30T23:24:46Z

README.md

+
+### Migration from pandas-ta
+
+If you're migrating from pandas-ta (abandoned since 2019), numta provides a compatible API:


The claim that pandas-ta was "abandoned since 2019" appears to be inaccurate. The pandas-ta library (twopirllc/pandas-ta on GitHub) has had active development beyond 2019, with the last release being in 2022. Consider revising this to simply state the migration benefits without making potentially inaccurate claims about the other library's maintenance status.

Suggested change

If you're migrating from pandas-ta (abandoned since 2019), numta provides a compatible API:

If you're migrating from pandas-ta, numta provides a compatible API:

Copilot · 2025-11-30T23:24:47Z

pyproject.toml

    "numba>=0.56.0",  # JIT compilation for faster execution
 ]
+all = [
+    "pandas>=1.3.0",


[nitpick] The "all" optional dependency group appears incomplete. It should include all optional dependencies (comparison, numba) or be more clearly named to indicate it only includes specific extras. Consider either:

Renaming to "dev-full" or similar to clarify scope

Including all optional dependencies: pandas, numba, comparison, pytest packages

Suggested change

"pandas>=1.3.0",

"pandas>=1.3.0",

"numba>=0.56.0",

"TA-Lib>=0.4.0",

Copilot · 2025-11-30T23:24:48Z

src/numta/pandas_ext.py

+            if not isinstance(result, tuple):
+                result = (result,)
+


[nitpick] Potential logic issue in _append_or_return: When column_names is a string, line 130 converts result to a tuple if it's not already one. However, lines 132-133 do the same check again. This creates redundant logic. While it shouldn't cause incorrect behavior (since the second check will see result is already a tuple), the code could be simplified by removing lines 132-133 since the tuple conversion is already handled in line 130 when needed.

Suggested change

if not isinstance(result, tuple):

result = (result,)

Initial plan

c5d04bc

Copilot AI assigned Copilot and deepentropy Nov 30, 2025

Copilot started work on behalf of deepentropy November 30, 2025 22:46 View session

Copilot AI and others added 2 commits November 30, 2025 23:07

Add pandas DataFrame extension accessor (.ta) for technical indicators

ca5650d

Co-authored-by: deepentropy <8287111+deepentropy@users.noreply.github.com>

Address code review feedback: update pyproject.toml and add note abou…

6da0550

…t pandas installation Co-authored-by: deepentropy <8287111+deepentropy@users.noreply.github.com>

Copilot AI changed the title ~~[WIP] Implement pandas DataFrame extension accessor for technical indicators~~ Add pandas DataFrame extension accessor (.ta) for technical indicators Nov 30, 2025

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deepentropy merged commit 95300f6 into main Nov 30, 2025
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Add pandas DataFrame extension accessor (.ta) for technical indicators #20

Add pandas DataFrame extension accessor (.ta) for technical indicators #20

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		### Migration from pandas-ta

		If you're migrating from pandas-ta (abandoned since 2019), numta provides a compatible API:

	If you're migrating from pandas-ta (abandoned since 2019), numta provides a compatible API:
	If you're migrating from pandas-ta, numta provides a compatible API:

Add pandas DataFrame extension accessor (.ta) for technical indicators #20

Add pandas DataFrame extension accessor (.ta) for technical indicators #20

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Current Indicator Signatures (from src/numta/api/overlap.py)

All Available Indicators (from src/numta/init.py)

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Copilot AI commented Nov 30, 2025 •

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