### Development Installation

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/README.md

Clone the repository and install with specific dependency groups for development or optional features.

```bash
# Clone the repository
git clone https://github.com/xgboosted/pandas-ta-classic.git
cd pandas-ta-classic

# Install with all dependencies
uv pip install -e ".[all]"

# Or install specific dependency groups:
uv pip install -e ".[dev]" # Development tools
uv pip install -e ".[optional]" # Optional runtime features
uv pip install -e ".[oracle]" # Oracle parity libs: TA-Lib + tulipy
```

```bash
# Clone the repository
git clone https://github.com/xgboosted/pandas-ta-classic.git
cd pandas-ta-classic

# Install with all dependencies
pip install -e ".[all]"

# Or install specific dependency groups:
pip install -e ".[dev]" # Development tools
pip install -e ".[optional]" # Optional runtime features
pip install -e ".[oracle]" # Oracle parity libs: TA-Lib + tulipy
```

--------------------------------

### Verify installation

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Run this script to confirm that the library is installed correctly and indicators are accessible.

```python
import pandas_ta_classic as ta
import pandas as pd

# Create a simple DataFrame
df = pd.DataFrame({'close': [100, 101, 102, 101, 100]})

# Test an indicator
sma = df.ta.sma(length=3)
print(sma)

# List all available indicators
print(f"Available indicators: {len(df.ta.indicators())}")
```

--------------------------------

### Install Required Packages

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/AIExample.ipynb

Uncomment and run these commands to install the necessary dependencies for the project.

```python
#!pip install numpy
#!pip install pandas
#!pip install mplfinance
#!pip install pandas-datareader
#!pip install requests_cache
#!pip install tqdm
#!pip install alphaVantage-api # Required for Watchlist
```

--------------------------------

### Development Installation (uv)

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Clone the repository and install in editable mode with specific dependency groups using uv.

```bash
# Clone the repository
git clone https://github.com/xgboosted/pandas-ta-classic.git
cd pandas-ta-classic

# Install in editable mode with all dependencies
uv pip install -e ".[all]"

# Or install specific dependency groups:
uv pip install -e ".[dev]"      # Development tools
uv pip install -e ".[test]"     # Testing: pytest, Hypothesis, coverage, benchmarks
uv pip install -e ".[optional]" # Optional features like TA-Lib
uv pip install -e ".[oracle]"   # Oracle parity libs: TA-Lib + tulipy
```

--------------------------------

### Install oracle libraries

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/README.md

Commands to install optional oracle libraries for testing and acceleration.

```bash
# uv
uv pip install pandas-ta-classic[oracle] # installs both TA-Lib and tulipy
uv pip install TA-Lib # TA-Lib only (also enables acceleration backend)
uv pip install tulipy # tulipy only (oracle test use only)
# pip
pip install pandas-ta-classic[oracle] # installs both TA-Lib and tulipy
pip install TA-Lib # TA-Lib only (also enables acceleration backend)
pip install tulipy # tulipy only (oracle test use only)
```

--------------------------------

### Setup Data and Indicators

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/tutorials.md

Download market data using yfinance and calculate baseline SMA and RSI indicators.

```python
import pandas as pd
import pandas_ta_classic as ta
import yfinance as yf

# Download data
df = yf.download("SPY", period="3mo")

# Calculate baseline indicators
df.ta.sma(length=50, append=True)
df.ta.rsi(length=14, append=True)
```

--------------------------------

### Installing tulipy

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Commands to install the tulipy oracle library.

```bash
uv pip install tulipy
```

```bash
pip install tulipy
```

--------------------------------

### Verify Installation

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/quickstart.md

Check if the package is installed or upgrade to the latest version.

```bash
pip list | grep pandas-ta-classic
# or
pip install --upgrade pandas-ta-classic
```

--------------------------------

### Install Development Version

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Install the latest features and bug fixes directly from the GitHub repository.

```bash
uv pip install git+https://github.com/xgboosted/pandas-ta-classic
```

```bash
pip install -U git+https://github.com/xgboosted/pandas-ta-classic
```

--------------------------------

### Development Installation (pip)

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Clone the repository and install in editable mode with specific dependency groups using pip.

```bash
# Clone the repository
git clone https://github.com/xgboosted/pandas-ta-classic.git
cd pandas-ta-classic

# Install in editable mode with all dependencies
pip install -e ".[all]"

# Or install specific dependency groups:
pip install -e ".[dev]"      # Development tools
pip install -e ".[test]"     # Testing: pytest, Hypothesis, coverage, benchmarks
pip install -e ".[optional]" # Optional features like TA-Lib
pip install -e ".[oracle]"   # Oracle parity libs: TA-Lib + tulipy
```

--------------------------------

### Install dependencies with uv

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/CONTRIBUTING.md

Commands to install the package in development mode with various dependency groups using uv.

```bash
# Install package in development mode with all dependencies
uv pip install -e ".[all]"

# Or install specific dependency groups as needed:
uv pip install -e ".[dev]"          # Development dependencies
uv pip install -e ".[test]"         # Testing: pytest, Hypothesis, coverage
uv pip install -e ".[docs]"         # Documentation dependencies
uv pip install -e ".[optional]"     # Optional runtime features (tqdm progress bars)
uv pip install -e ".[oracle]"       # Oracle parity libs: TA-Lib + tulipy
uv pip install -e ".[integration]"  # Backtesting integrations: backtesting, backtrader, vectorbt, yfinance
uv pip install -e ".[performance]"  # Numba acceleration
```

--------------------------------

### Install VectorBT

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/tutorials.md

Install the required VectorBT package via pip.

```bash
pip install vectorbt
```

--------------------------------

### Install TA-Lib Acceleration

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/quickstart.md

Install the optional TA-Lib dependency to accelerate core indicators.

```bash
pip install "ta-lib>=0.6.8"
```

--------------------------------

### Installing Oracle Libraries via Extras

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Convenience command to install both oracle libraries simultaneously.

```bash
# uv
uv pip install pandas-ta-classic[oracle]

# pip
pip install pandas-ta-classic[oracle]
```

--------------------------------

### Installing TA-Lib

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Commands to install the TA-Lib library using various package managers.

```bash
uv pip install "ta-lib>=0.6.8"
```

```bash
pip install "ta-lib>=0.6.8"
```

```bash
conda install -c conda-forge libta-lib
conda install -c conda-forge ta-lib
```

--------------------------------

### Install Stable Release

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Install the latest stable version from PyPI using recommended or standard package managers.

```bash
uv pip install pandas-ta-classic
```

```bash
pip install pandas-ta-classic
```

--------------------------------

### Install optional dependencies with uv

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Use these commands to install optional packages when using the uv package manager.

```bash
# For stock data download
uv pip install yfinance

# For backtesting
uv pip install backtesting
uv pip install backtrader
uv pip install vectorbt

# For enhanced performance (optional — provides 6–230× speedups on hot-loop indicators)
uv pip install pandas-ta-classic[performance]

# Or install numba directly
uv pip install numba

# Install all optional dependencies at once
uv pip install pandas-ta-classic[optional]
```

--------------------------------

### Initialize AlphaVantage Data Source

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/PandasTA_Strategy_Examples.ipynb

Configure the AlphaVantage data source if the library is installed.

```python
if AlphaVantage is not None:
    AV = AlphaVantage(
        api_key="YOUR API KEY",
        premium=False,
        output_size="full",
        clean=True,
        export_path=".",
        export=True,
    )
else:
    AV = None
    print("[!] AlphaVantage not installed. Install with: pip install alphaVantage-api")
AV
```

--------------------------------

### Install dependencies with pip

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/CONTRIBUTING.md

Commands to install the package in development mode with various dependency groups using pip.

```bash
# Install package in development mode with all dependencies
pip install -e ".[all]"

# Or install specific dependency groups as needed:
pip install -e ".[dev]"          # Development dependencies
pip install -e ".[test]"         # Testing: pytest, Hypothesis, coverage
pip install -e ".[docs]"         # Documentation dependencies
pip install -e ".[optional]"     # Optional runtime features (tqdm progress bars)
pip install -e ".[oracle]"       # Oracle parity libs: TA-Lib + tulipy
pip install -e ".[integration]"  # Backtesting integrations: backtesting, backtrader, vectorbt, yfinance
pip install -e ".[performance]"  # Numba acceleration
```

--------------------------------

### Quick Start and Indicator Calculation

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/index.md

Basic usage for loading data, calculating indicators, and using the fluent API or strategies.

```python
import pandas as pd
import pandas_ta_classic as ta

# Load your data
df = pd.read_csv("path/to/symbol.csv")

# Calculate indicators
df.ta.sma(length=20, append=True)  # Simple Moving Average
df.ta.rsi(length=14, append=True)  # RSI
df.ta.macd(append=True)            # MACD

# Fluent API chaining
df.ta.chain().sma(20).ta.rsi(14).ta.macd().ta.bbands(20)

# Or use strategies for bulk processing
df.ta.strategy("CommonStrategy")
```

--------------------------------

### Install optional dependencies with pip

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Use these commands to install optional packages when using the standard pip package manager.

```bash
# For stock data download
pip install yfinance

# For backtesting
pip install backtesting
pip install backtrader
pip install vectorbt

# For enhanced performance (optional — provides 6–230× speedups on hot-loop indicators)
pip install pandas-ta-classic[performance]

# Or install numba directly
pip install numba

# Install all optional dependencies at once
pip install pandas-ta-classic[optional]
```

--------------------------------

### Get Indicator Help

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/example.ipynb

Accesses documentation for a specific indicator.

```python
help(ta.ema)
```

--------------------------------

### Configure TA-Lib Implementation for Core Indicators

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/indicators.md

Native implementations are used by default for core indicators. Pass talib=True to explicitly use the TA-Lib implementation if installed.

```python
 # Uses native EMA (default behaviour)
 ema = df.ta.ema(length=20)

 # Use TA-Lib implementation if installed
 ema = df.ta.ema(length=20, talib=True)

 # Indicators with TA-Lib passthrough:
# ad, adosc, apo, aroon, atr, bbands, bop, cci, cmo, dema, dm,
# ema, hlc3, macd, mfi, midpoint, midprice, mom, natr, obv, ppo,
# roc, rsi, sma, stdev, t3, tema, trima, true_range, uo,
# variance, wcp, willr, wma
```

--------------------------------

### Initialize Environment and Dependencies

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Imports necessary libraries and prints version information to verify the environment.

```python
from datetime import datetime


import numpy as np
import pandas as pd
import pandas_ta_classic as ta
import vectorbt as vbt


print("Package Versions:")
print(f"Numpy v{np.__version__}")
print(f"Pandas v{pd.__version__}")
print("vectorbt >= v1.0.0")
print(
    f"\nPandas TA Classic v{ta.version}\nTo install the Latest Version:\n$ pip install pandas-ta-classic\n"
)

%matplotlib inline
```

--------------------------------

### Import Libraries and Download Data

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/tutorials.md

Initialize the environment by importing necessary libraries and fetching historical price data.

```python
import pandas as pd
import pandas_ta_classic as ta
import vectorbt as vbt
import yfinance as yf

# Download data
df = yf.download("AAPL", start="2022-01-01", end="2024-01-01")
```

--------------------------------

### Initialize Environment and Imports

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/AIExample.ipynb

Import core libraries and configure Pandas display settings for data analysis.

```python
import numpy as np
import pandas as pd
import mplfinance as mpf
import pandas_ta_classic as ta

from watchlist import (
    colors,
    Watchlist,
)  # Is this failing? If so, copy it locally. See above.

pd.set_option("display.max_rows", 100)
pd.set_option("display.max_columns", 20)

print(f"Numpy v{np.__version__}")
print(f"Pandas v{pd.__version__}")
print(f"mplfinance v{mpf.__version__}")
print(
    f"\nPandas TA Classic v{ta.version}\nTo install the Latest Version:\n$ pip install pandas-ta-classic\n"
)
%matplotlib inline
```

--------------------------------

### Set Up Environment for Pandas TA Classic

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/tutorials.md

Imports necessary libraries and downloads historical stock data using yfinance.

```python
import pandas as pd
import pandas_ta_classic as ta
import yfinance as yf
import matplotlib.pyplot as plt

# Download data
df = yf.download("AAPL", start="2023-01-01", end="2024-01-01")
print(f"Downloaded {len(df)} rows of data")
```

--------------------------------

### Initialize Environment

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/example.ipynb

Imports necessary libraries and configures the environment for technical analysis.

```python
%matplotlib inline
import random as rnd

import numpy as np
import pandas as pd
import mplfinance as mpf

# Optional import for AlphaVantage API
try:
    from alphaVantageAPI.alphavantage import AlphaVantage
except ImportError:
    print(
        "[!] alphaVantageAPI not available. Install with: pip install alphaVantage-api"
    )
    AlphaVantage = None
import pandas_ta_classic as ta

from watchlist import colors  # Is this failing? If so, copy it locally. See above.

print(
    f"\nPandas TA Classic v{ta.version}\nTo install the Latest Version:\n$ pip install pandas-ta-classic\n"
)
```

--------------------------------

### Create virtual environment

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/CONTRIBUTING.md

Commands to initialize a virtual environment using either uv or standard venv.

```bash
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
```

```bash
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
```

--------------------------------

### Clone the repository

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/CONTRIBUTING.md

Use a full clone to ensure setuptools-scm can access git tags for versioning.

```bash
git clone https://github.com/xgboosted/pandas-ta-classic.git
```

--------------------------------

### Create Buy and Hold Portfolios

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Initializes portfolios based on holding strategies for benchmark and asset data.

```python
# Benchmark Buy and Hold (BnH) Strategy
benchmarkpf_bnh = vbt.Portfolio.from_holding(benchmarkdf.close)
print(trade_table(benchmarkpf_bnh))
combine_stats(benchmarkpf_bnh, benchmarkdf.name, "Buy and Hold", LIVE)
```

```python
# Asset Buy and Hold (BnH) Strategy
assetpf_bnh = vbt.Portfolio.from_holding(assetdf.close)
print(trade_table(assetpf_bnh))
combine_stats(assetpf_bnh, assetdf.name, "Buy and Hold", LIVE)
```

--------------------------------

### Create Signal-based Portfolios

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Initializes portfolios using entry and exit signals.

```python
# Benchmark Portfolio from Trade Signals
benchmarkpf_signals = vbt.Portfolio.from_signals(
    benchmarkdf.close,
    entries=benchmark_signals.TS_Entries,
    exits=benchmark_signals.TS_Exits,
)
trade_table(benchmarkpf_signals, k=5)
combine_stats(benchmarkpf_signals, benchmarkdf.name, "Long Strategy", LIVE)
```

```python
# Asset Portfolio from Trade Signals
assetpf_signals = vbt.Portfolio.from_signals(
    assetdf.close,
    entries=asset_signals.TS_Entries,
    exits=asset_signals.TS_Exits,
)
trade_table(assetpf_signals, k=5)
combine_stats(assetpf_signals, assetdf.name, "Long Strategy", LIVE)
```

--------------------------------

### Create and Inspect Watchlist

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/PandasTA_Strategy_Examples.ipynb

Initialize a Watchlist with tickers and inspect its configuration.

```python
data_source = "yahoo"  # Default (change to "av" if AlphaVantage is configured)
# data_source = "av"
watch = Watchlist(["SPY", "IWM"], ds_name=data_source, timed=False)
```

```python
watch
```

```python
help(Watchlist)
```

--------------------------------

### Define Backtest Date Range

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Set the start and end dates for the analysis period.

```python
start_date = datetime(2005, 1, 1)  # Adjust as needed
end_date = datetime(2010, 1, 1)  # Adjust as needed
```

--------------------------------

### Get List of Indicators

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/dataframe_api.md

Retrieve a list of available technical indicators. Can be returned as a list or exclude specific indicators.

```python
ind_list = df.ta.indicators(as_list=True)
```

```python
ind_list = df.ta.indicators(as_list=True, exclude=["vp", "td_seq"])
```

--------------------------------

### Configuring and running a backtest

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/tutorials/vectorbt.md

Sets portfolio settings and executes the backtest using generated signals.

```python
vbt.settings.portfolio["freq"] = "1D"
vbt.settings.portfolio["fees"] = 0.0025
vbt.settings.portfolio["slippage"] = 0.0025

pf = vbt.Portfolio.from_signals(
    df["Close"],
    entries=signals.TS_Entries,
    exits=signals.TS_Exits,
)

print(pf.stats())
```

--------------------------------

### Fetch data using built-in ticker method

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/quickstart.md

Uses the library's internal ticker wrapper which requires yfinance to be installed.

```python
import pandas as pd
import pandas_ta_classic as ta

# Create empty DataFrame
df = pd.DataFrame()

# Fetch data (requires yfinance)
df = df.ta.ticker("AAPL", period="1y")

# Add indicators
df.ta.sma(length=20, append=True)
df.ta.ema(length=20, append=True)
```

--------------------------------

### Property: Get Last Run Time

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/dataframe_api.md

The `.ta.last_run` property returns the execution time in seconds of the most recently run indicator or strategy.

```python
# Returns the time it took to run the last indicator or strategy
df.ta.last_run
```

--------------------------------

### Using TA-Lib for Core Indicators

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/installation.md

Demonstrates how to toggle between native implementations and TA-Lib acceleration for core indicators.

```python
import pandas_ta_classic as ta

# Uses native EMA — default behaviour
ema = df.ta.ema(length=20)

# Use TA-Lib implementation if installed
ema = df.ta.ema(length=20, talib=True)

# CDL patterns — always native, talib= kwarg has no effect here
df = df.ta.cdl_pattern(name="engulfing")       # native
result = df.ta.cdl_pattern(name="hammer")      # native
```

--------------------------------

### Define indicator docstring format

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/CONTRIBUTING.md

Required structure for indicator function docstrings, including type hints, argument descriptions, and usage examples.

```python
from typing import Optional

def indicator_name(close: pd.Series, length: Optional[int] = None) -> pd.Series:
 """
 Brief description of the indicator.
 
 Longer description if needed, including mathematical formula
 or algorithm explanation.
 
 Args:
 close (pd.Series): Series of closing prices
 length (Optional[int]): Lookback period. Defaults to None (resolved internally, typically 20).
 
 Returns:
 pd.Series: Calculated indicator values
 
 Example:
 >>> import pandas as pd
 >>> import pandas_ta_classic as ta
 >>> data = pd.read_csv('data.csv')
 >>> result = ta.indicator_name(data['close'])
 """
```

--------------------------------

### Initialize Pandas TA Classic Environment

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/PandasTA_Strategy_Examples.ipynb

Sets up the environment by importing necessary modules and checking for optional dependencies like AlphaVantage and the Watchlist module.

```python
%matplotlib inline


import pandas_ta_classic as ta

# Optional import for AlphaVantage API
try:
    from alphaVantageAPI.alphavantage import (
        AlphaVantage,
    )  # pip install alphaVantage-api
except ImportError:
    print(
        "[!] alphaVantageAPI not available. Install with: pip install alphaVantage-api"
    )
    AlphaVantage = None

from watchlist import Watchlist  # Is this failing? If so, copy it locally. See above.

print(
    f"\nPandas TA Classic v{ta.version}\nTo install the Latest Version:\n$ pip install pandas-ta-classic\n"
)
```

--------------------------------

### Configure VectorBT Theme and Portfolio Settings

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Sets global visualization themes and portfolio parameters such as fees, slippage, and frequency.

```python
cheight, cwidth = 500, 1000  # Adjust as needed for Chart Height and Width
vbt.settings.set_theme("dark")  # Options: "light" (Default), "dark" (my fav), "seaborn"

# Must be set
vbt.settings.portfolio["freq"] = "1D"  # Daily

# Predefine vectorbt Portfolio settings
# vbt.settings.portfolio["init_cash"] = 100
vbt.settings.portfolio["fees"] = 0.0025  # 0.25%
vbt.settings.portfolio["slippage"] = 0.0025  # 0.25%
# vbt.settings.portfolio["size"] = 100
# vbt.settings.portfolio["accumulate"] = False
vbt.settings.portfolio["allow_partial"] = False

pf_settings = pd.DataFrame(vbt.settings.portfolio.items(), columns=["Option", "Value"])
pf_settings.set_index("Option", inplace=True)

print("Portfolio Settings [Initial]")
pf_settings
```

--------------------------------

### Clone the repository

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/CONTRIBUTING.md

Commands to clone the forked repository and navigate into the project directory.

```bash
git clone https://github.com/your-username/pandas-ta-classic.git
cd pandas-ta-classic
```

--------------------------------

### Access Squeeze Indicator Help

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/example.ipynb

Displays the help documentation for the squeeze indicator function.

```python
# help(ta.squeeze)
```

--------------------------------

### Define Tickers for Benchmarks and Assets

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Initialize lists for benchmark and asset symbols to be used in the backtest.

```python
benchmark_tickers = ["SPY", "QQQ"]
asset_tickers = ["AAPL", "TSLA", "NVDA"]
all_tickers = benchmark_tickers + asset_tickers

print("Tickers by index #")
print("=" * 100)
print(f"Benchmarks: {', '.join([f'{k}: {v}' for k,v in enumerate(benchmark_tickers)])}")
print(f"    Assets: {', '.join([f'{k}: {v}' for k,v in enumerate(asset_tickers)])}")
print(f"       All: {', '.join([f'{k}: {v}' for k,v in enumerate(all_tickers)])}")
```

```python
benchmark = benchmark_tickers[0]  # Change index for different benchmark
asset = asset_tickers[2]  # Change index for different symbol
print(f"Selected Benchmark | Asset: {benchmark} | {asset}")
```

--------------------------------

### Display Available Data

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Print the keys of the downloaded benchmark and asset datasets.

```python
print("Available Data:")
print("=" * 100)
print(f"Benchmarks: {', '.join(benchmarks.keys())}")
print(f"Assets: {', '.join(assets.keys())}")
```

--------------------------------

### Execute Full Test Suite

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/testing.md

Commands for running the complete test suite, regenerating fixtures, and checking code coverage.

```bash
# Full test suite (primary — matches CI, auto-regenerates fixture JSONs)
python -m unittest discover tests/ -v

# Regenerate fixtures then run all tests (recommended after indicator changes)
make test-all

# Regenerate fixture JSONs only (requires TA-Lib installed)
make fixtures

# pytest equivalent
python -m pytest tests/ -v

# With coverage
python -m pytest --cov=pandas_ta_classic --cov-report=html tests/
```

--------------------------------

### Create Custom Strategies

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/PandasTA_Strategy_Examples.ipynb

Define strategies by providing a name and a list of indicator dictionaries.

```python
custom_a = ta.Strategy(
    name="A", ta=[{"kind": "sma", "length": 50}, {"kind": "sma", "length": 200}]
)
custom_a
```

```python
custom_b = ta.Strategy(
    name="B",
    ta=[
        {"kind": "ema", "length": 8},
        {"kind": "ema", "length": 21},
        {"kind": "log_return", "cumulative": True},
        {"kind": "rsi"},
        {"kind": "supertrend"},
    ],
)
custom_b
```

--------------------------------

### Implement Charting Class with mplfinance

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/example.ipynb

A custom class structure for integrating technical analysis strategies with mplfinance visualization.

```python
class Chart(object):
    def __init__(
        self,
        df: pd.DataFrame = None,
        strategy: ta.Strategy = ta.CommonStrategy,
        *args,
        **kwargs,
    ):
        self.verbose = kwargs.pop("verbose", False)

        if isinstance(df, pd.DataFrame) and df.ta.datetime_ordered:
            self.df = df
            if self.df.name is not None and self.df.name != "":
                df_name = str(self.df.name)
            else:
                df_name = "DataFrame"
            if self.verbose:
                print(f"[i] Loaded {df_name}{self.df.shape}")
        else:
            print("[X] Oops! Missing 'ohlcv' data or index is not datetime ordered.\n")
            return None

        self._validate_mpf_kwargs(**kwargs)
        self._validate_chart_kwargs(**kwargs)
        self._validate_ta_strategy(strategy)

        # Build TA and Plot
        self.df.ta.strategy(self.strategy, verbose=self.verbose)
        self._plot(**kwargs)

    def _validate_ta_strategy(self, strategy):
        if strategy is not None or isinstance(strategy, ta.Strategy):
            self.strategy = strategy
        elif len(self.strategy_ta) > 0:
            print(f"[+] Strategy: {self.strategy_name}")
        else:
            self.strategy = ta.CommonStrategy

    def _validate_chart_kwargs(self, **kwargs):
        """Chart Settings"""
        self.config = {}
        self.config["last"] = kwargs.pop("last", recent_bars(self.df))
        self.config["rpad"] = kwargs.pop("rpad", 10)
        self.config["title"] = kwargs.pop("title", "Asset")
        self.config["volume"] = kwargs.pop("volume", True)

    def _validate_mpf_kwargs(self, **kwargs):
        # mpf global chart settings
        default_mpf_width = {
            "candle_linewidth": 0.6,
            "candle_width": 0.525,
            "volume_width": 0.525,
        }
        mpfchart = {}

        mpf_style = kwargs.pop("style", "")
        if mpf_style == "" or mpf_style.lower() == "random":
            mpf_styles = mpf.available_styles()
            mpfchart["style"] = mpf_styles[rnd.randrange(len(mpf_styles))]
        elif mpf_style.lower() in mpf.available_styles():
            mpfchart["style"] = mpf_style

        mpfchart["figsize"] = kwargs.pop("figsize", (12, 10))
        mpfchart["non_trading"] = kwargs.pop("nontrading", False)
        mpfchart["rc"] = kwargs.pop("rc", {"figure.facecolor": "#EDEDED"})
        mpfchart["plot_ratios"] = kwargs.pop("plot_ratios", (12, 1.7))
        mpfchart["scale_padding"] = kwargs.pop(
            "scale_padding", {"left": 1, "top": 4, "right": 1, "bottom": 1}
        )
        mpfchart["tight_layout"] = kwargs.pop("tight_layout", True)
        mpfchart["type"] = kwargs.pop("type", "candle")
        mpfchart["width_config"] = kwargs.pop("width_config", default_mpf_width)
        mpfchart["xrotation"] = kwargs.pop("xrotation", 15)

        self.mpfchart = mpfchart

    def _attribution(self):
        print(
            f"\nPandas v: {pd.__version__} [pip install pandas] https://github.com/pandas-dev/pandas"
        )
        print(
            "Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co"
        )
        print(
            f"Technical Analysis with Pandas TA Classic v: {ta.version} [pip install pandas-ta-classic] https://github.com/xgboosted/pandas-ta-classic"
        )
        print(
            f"Charts by Matplotlib Finance v: {mpf.__version__} [pip install mplfinance] https://github.com/matplotlib/mplfinance\n"
        )

    def _right_pad_df(self, rpad: int, delta_unit: str = "D", range_freq: str = "B"):
        if rpad > 0:
            dfpad = self.df[-rpad:].copy()
            dfpad.iloc[:, :] = np.nan

            df_frequency = self.df.index.value_counts().mode()[
                0
            ]  # Most common frequency
            freq_delta = pd.Timedelta(df_frequency, unit=delta_unit)
            new_dr = pd.date_range(
                start=self.df.index[-1] + freq_delta, periods=rpad, freq=range_freq
            )
            dfpad.index = new_dr  # Update the padded index with new dates
            self.df = pd.concat([self.df, dfpad])

    def _plot(self, **kwargs):
        if not isinstance(self.mpfchart["plot_ratios"], tuple):
            print("[X] plot_ratios must be a tuple")
            return
```

--------------------------------

### Swap and Run Strategies

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/PandasTA_Strategy_Examples.ipynb

Assign custom strategies to the Watchlist and execute them on specific tickers.

```python
# Load custom_a into Watchlist and verify
watch.strategy = custom_a
# watch.debug = True
watch.strategy
```

```python
watch.load("IWM")
```

```python
# Load custom_b into Watchlist and verify
watch.strategy = custom_b
watch.strategy
```

```python
watch.load("SPY")
```

--------------------------------

### Execute Watchlist Strategy

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/PandasTA_Strategy_Examples.ipynb

Load data for tickers using the assigned strategy.

```python
# No arguments loads all the tickers and applies the Strategy to each ticker.
# The result can be accessed with Watchlist's 'data' property which returns a
# dictionary keyed by ticker and DataFrames as values
watch.load(verbose=True)
```

```python
", ".join([f"{t}: {d.shape}" for t, d in watch.data.items()])
```

```python
watch.data["SPY"]
```

```python
watch.load("SPY", plot=True, mas=True)
```

--------------------------------

### Development Validation Commands

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/AGENTS.md

Commands for setting up the development environment, running tests, and performing code quality checks.

```bash
# Create venv (first time only)
python -m venv .venv        # stdlib
# or: uv venv .venv         # faster alternative if uv installed

# Activate and install
source .venv/bin/activate
pip install -e .             # or: uv pip install -e .

# Core import check
python -c "import pandas_ta_classic; print(pandas_ta_classic.version)"

# Formatting check
black --check --diff pandas_ta_classic/

# Linting (critical errors only)
ruff check pandas_ta_classic --select E9,F63,F7,F82

# Linting (advisory — non-blocking in CI)
ruff check pandas_ta_classic --extend-select C901,E501 --exit-zero

# Apply formatting
black pandas_ta_classic/

# Install oracle dependencies (required for full test suite)
pip install -e ".[oracle]"

# Full test suite
pytest tests/ -v

# Oracle tests only
pytest tests/test_oracle_talib.py tests/test_oracle_tulipy.py -v

# Single test module (fastest feedback)
pytest tests/test_indicator_momentum.py -v

# Specific test
pytest tests/test_indicator_momentum.py::TestRSI::test_rsi -v

# Hypothesis property-based tests
pytest tests/test_property_based.py -v

# Docs build
cd docs && make html

# Build distribution
python -m build
```

--------------------------------

### Comparing strategy against Buy-and-Hold

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/tutorials/vectorbt.md

Benchmarks the strategy performance against a simple buy-and-hold portfolio.

```python
pf_bnh = vbt.Portfolio.from_holding(df["Close"])

print("Strategy:")
print(pf.stats()[["Total Return [%]", "Sharpe Ratio", "Max Drawdown [%]"]])

print("\nBuy and Hold:")
print(pf_bnh.stats()[["Total Return [%]", "Sharpe Ratio", "Max Drawdown [%]"]])
```

--------------------------------

### Define and Run Custom Strategy

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/strategies.md

Creates and runs a custom strategy by defining a name, description, and a list of indicators with their specific arguments. This allows for a tailored set of indicators to be applied.

```python
CustomStrategy = ta.Strategy(
    name="Momo and Volatility",
    description="SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20",
    ta=[
        {"kind": "sma", "length": 50},
        {"kind": "sma", "length": 200},
        {"kind": "bbands", "length": 20},
        {"kind": "rsi"},
        {"kind": "macd", "fast": 8, "slow": 21},
        {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"},
    ]
)
# To run your "Custom Strategy"
df.ta.strategy(CustomStrategy)
```

--------------------------------

### Run VectorBT Backtest

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/tutorials.md

Execute the backtest using the generated signals and display performance statistics.

```python
# Create portfolio
portfolio = vbt.Portfolio.from_signals(
 df['Close'],
 entries,
 exits,
 init_cash=10000,
 fees=0.001, # 0.1% trading fees
 freq='1D'
)

# Display results
print(portfolio.stats())
print(f"\nTotal Return: {portfolio.total_return() * 100:.2f}%")
print(f"Sharpe Ratio: {portfolio.sharpe_ratio():.2f}")
print(f"Max Drawdown: {portfolio.max_drawdown() * 100:.2f}%")
```

--------------------------------

### Create and push a release tag

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/CONTRIBUTING.md

Create an annotated tag and push it to the remote repository to trigger the automated release process.

```bash
git tag -a X.Y.Z -m "X.Y.Z"
git push origin X.Y.Z
```

--------------------------------

### Fix versioning issues

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/CONTRIBUTING.md

Fetch missing tags and reinstall the package in editable mode to regenerate the version file if the version appears as 0.0.0.

```bash
git fetch --tags
pip install -e ".[dev]"  # reinstall to regenerate _version.py
```

--------------------------------

### Load data from CSV files

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/quickstart.md

Demonstrates loading OHLCV data from a local file and ensuring proper formatting for indicator calculation.

```python
import pandas as pd
import pandas_ta_classic as ta

# Load CSV with OHLCV data
df = pd.read_csv('stock_data.csv', parse_dates=['date'], index_col='date')

# Ensure column names are lowercase (optional but recommended)
df.columns = [col.lower() for col in df.columns]

# Calculate indicators
df.ta.sma(length=20, append=True)
df.ta.atr(length=14, append=True) # Average True Range
```

--------------------------------

### Retrieve Indicator Documentation

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/quickstart.md

Use the help function or access the docstring directly to understand indicator parameters.

```python
# Get indicator documentation
help(ta.sma)
help(ta.macd)
help(ta.rsi)

# Or use Python's built-in
print(ta.sma.__doc__)
```

--------------------------------

### Configure and Plot Technical Indicators with mplfinance

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/example.ipynb

This snippet demonstrates how to override chart configurations, calculate various technical indicators, and prepare them for visualization using mplfinance addplots.

```python
# Override Chart Title Option
        chart_title = self.config["title"]
        if "title" in kwargs and isinstance(kwargs["title"], str):
            chart_title = kwargs.pop("title")

        # Override Right Bar Padding Option
        rpad = self.config["rpad"]
        if "rpad" in kwargs and kwargs["rpad"] > 0:
            rpad = int(kwargs["rpad"])

        def cpanel():
            return len(self.mpfchart["plot_ratios"])

        # Last Second Default TA Indicators
        linreg = kwargs.pop("linreg", False)
        linreg_name = self.df.ta.linreg(append=True).name if linreg else ""

        midpoint = kwargs.pop("midpoint", False)
        midpoint_name = self.df.ta.midpoint(append=True).name if midpoint else ""

        ohlc4 = kwargs.pop("ohlc4", False)
        ohlc4_name = self.df.ta.ohlc4(append=True).name if ohlc4 else ""

        clr = kwargs.pop("clr", False)
        clr_name = (
            self.df.ta.log_return(cumulative=True, append=True).name if clr else ""
        )

        rsi = kwargs.pop("rsi", False)
        rsi_length = kwargs.pop("rsi_length", None)
        if isinstance(rsi_length, int) and rsi_length > 1:
            rsi_name = self.df.ta.rsi(length=rsi_length, append=True).name
        elif rsi:
            rsi_name = self.df.ta.rsi(append=True).name
        else:
            rsi_name = ""

        zscore = kwargs.pop("zscore", False)
        zscore_length = kwargs.pop("zscore_length", None)
        if isinstance(zscore_length, int) and zscore_length > 1:
            zs_name = self.df.ta.zscore(length=zscore_length, append=True).name
        elif zscore:
            zs_name = self.df.ta.zscore(append=True).name
        else:
            zs_name = ""

        macd = kwargs.pop("macd", False)
        macd_name = ""
        if macd:
            macds = self.df.ta.macd(append=True)
            macd_name = macds.name

        squeeze = kwargs.pop("squeeze", False)
        lazybear = kwargs.pop("lazybear", False)
        squeeze_name = ""
        if squeeze:
            squeezes = self.df.ta.squeeze(lazybear=lazybear, detailed=True, append=True)
            squeeze_name = squeezes.name

        ama = kwargs.pop("archermas", False)
        ama_name = ""
        if ama:
            amas = self.df.ta.amat(append=True)
            ama_name = amas.name

        aobv = kwargs.pop("archerobv", False)
        aobv_name = ""
        if aobv:
            aobvs = self.df.ta.aobv(append=True)
            aobv_name = aobvs.name

        # Pad and trim Chart
        self._right_pad_df(rpad)
        mpfdf = self.df.tail(self.config["last"]).copy()
        mpfdf_columns = list(self.df.columns)

        tsig = kwargs.pop("tsignals", False)
        if tsig:
            # Long Trend requires Series Comparison (<=. <, = >, >=)
            # or Trade Logic that yields trends in binary.
            default_long = mpfdf["SMA_10"] > mpfdf["SMA_20"]
            long_trend = kwargs.pop("long_trend", default_long)
            if not isinstance(long_trend, pd.Series):
                raise (
                    "[X] Must be a Series that has boolean values or values of 0s and 1s"
                )
            mpfdf.ta.percent_return(append=True)
            mpfdf.ta.tsignals(long_trend, append=True)
            buys = np.where(mpfdf.TS_Entries > 0, 1, np.nan)
            sells = np.where(mpfdf.TS_Exits > 0, 1, np.nan)
            mpfdf["ACTRET_1"] = mpfdf.TS_Trends * mpfdf.PCTRET_1

        # BEGIN: Custom TA Plots and Panels
        # Modify the area below
        taplots = []  # Holds all the additional plots

        # Panel 0: Price Overlay
        if linreg_name in mpfdf_columns:
            taplots += [
                mpf.make_addplot(
                    mpfdf[linreg_name],
                    type=kwargs.pop("linreg_type", "line"),
                    color=kwargs.pop("linreg_color", "black"),
                    linestyle="-.",
                    width=1.2,
                    panel=0,
                )
            ]

        if midpoint_name in mpfdf_columns:
            taplots += [
                mpf.make_addplot(
                    mpfdf[midpoint_name],
                    type=kwargs.pop("midpoint_type", "scatter"),
                    color=kwargs.pop("midpoint_color", "fuchsia"),
                    width=0.4,
                    panel=0,
                )
            ]

        if ohlc4_name in mpfdf_columns:
            taplots += [
                mpf.make_addplot(
                    mpfdf[ohlc4_name],
                    ylabel=ohlc4_name,
                    type=kwargs.pop("ohlc4_type", "scatter"),
                    color=kwargs.pop("ohlc4_color", "blue"),
                    alpha=0.85,
                    width=0.4,
                    panel=0,
                )
            ]
```

--------------------------------

### Implement Momentum Strategy with Multi-Output Indicators

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/tutorials/backtesting_py.md

Unpacks multi-column indicator results like MACD into separate strategy attributes.

```python
class MACDMomentum(Strategy):
    def init(self):
        self.macd_line, self.macd_hist, self.macd_signal = self.I(
            ta_bridge, self.data, lambda df: df.ta.macd(fast=12, slow=26, signal=9)
        )

    def next(self):
        if crossover(self.macd_line, self.macd_signal):
            self.buy()
        elif crossover(self.macd_signal, self.macd_line):
            self.position.close()
```

--------------------------------

### Apply support and resistance indicators

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/quickstart.md

Calculates Parabolic SAR and Donchian Channels to identify key price levels.

```python
# Parabolic Stop and Reverse
df.ta.psar(append=True)

# Donchian Channel (high/low price channels)
df.ta.donchian(lower_length=20, upper_length=20, append=True)
```

--------------------------------

### Access AllStrategy Properties

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/PandasTA_Strategy_Examples.ipynb

Retrieves and prints the metadata and indicator configuration for the built-in AllStrategy.

```python
AllStrategy = ta.AllStrategy
print("name =", AllStrategy.name)
print("description =", AllStrategy.description)
print("created =", AllStrategy.created)
print("ta =", AllStrategy.ta)
```

--------------------------------

### Configure Plotting Environment

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Sets the theme and patches the show_png method to support interactive plotly widgets in notebooks.

```python
vbt.settings.set_theme("seaborn")
```

```python
# Patch show_png to display as interactive plotly widget (no Chrome/kaleido needed)
import plotly.io._renderers as _pr
import nbformat as _nbf
import vectorbt.utils.figure as _vbt_fig

# Force plotly to recognize nbformat is available
_pr.nbformat = _nbf

def _show_png_patched(self, **kw):
    import plotly.io as pio
    pio.show(self, renderer="notebook_connected")

_vbt_fig.FigureMixin.show_png = _show_png_patched
```

--------------------------------

### Run multiple indicators using the Strategy System

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/quickstart.md

Efficiently execute multiple indicators at once using built-in or custom strategies.

```python
import pandas as pd
import pandas_ta_classic as ta

df = pd.read_csv('your_data.csv')

# Use a built-in strategy
df.ta.strategy("CommonStrategy")

# Or create your own custom strategy
my_strategy = ta.Strategy(
 name="MyStrategy",
 ta=[
 {"kind": "sma", "length": 20},
 {"kind": "sma", "length": 50},
 {"kind": "rsi", "length": 14},
 {"kind": "macd", "fast": 12, "slow": 26, "signal": 9},
 {"kind": "bbands", "length": 20},
 ]
)

# Run your strategy
df.ta.strategy(my_strategy)
print(df.columns) # See all new indicator columns
```

--------------------------------

### Generate Fixture Files

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/docs/testing.md

Manually trigger the generation of expected values and regression snapshots.

```bash
python -m tests.fixtures.generate_fixtures
python -m tests.fixtures.generate_regression_snapshots
```

--------------------------------

### Select and Filter Data for Backtesting

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/VectorBT_Backtest_with_Pandas_TA.ipynb

Select specific assets and apply date masking to constrain the data range.

```python
benchmark_name = "SPY"  # Select a Benchmark
asset_name = "AAPL"  # Select an Asset

benchmarkdf = benchmarks[benchmark_name]
assetdf = assets[asset_name]

# Set True if you want to constrain Data between start_date & end_date
common_range = True
if common_range:
    crs = f" from {start_date} to {end_date}"
    benchmarkdf = dtmask(benchmarkdf, start_date, end_date)
    assetdf = dtmask(assetdf, start_date, end_date)

# Update DataFrame names
benchmarkdf.name = benchmark_name
assetdf.name = asset_name
print(
    f"Analysis of: {benchmarkdf.name} and {assetdf.name}{crs if common_range else ''}"
)
```

```python
benchmarkdf
```

```python
assetdf
```

--------------------------------

### Check Ticker Help

Source: https://github.com/xgboosted/pandas-ta-classic/blob/main/examples/example.ipynb

Placeholder for checking ticker-related help documentation.

```python
# help(e.ta.ticker)
```