PyChanLun 0.1.5

Applying the Chan Theory (Chan Lun) to analyze stock trading information.

PyChanLun

Applying the Chan Theory (Chan Lun) to analyze stock trading information.

What is PyChanLun?

PyChanLun is a Python library designed to perform technical analysis on stock market data using the principles of Chan Theory.

It implements a hierarchical structure of core Chan Theory concepts, starting from raw price data and building up through:

1. Sticks (Stick)
2. Fractals (Fractal)
3. Strokes (Stroke)
4. Segments (Segment)
5. Pivots (Pivot)
6. Signals (Signal)

The library processes time-series data to identify key structural components and generate trading-related data like pivots, trend levels, and trading signals, combining price action with indicators like MACD.

Where is the data coming from?

PyChanLun uses the yfinance library to download historical stock data.

• The data acquisition logic is encapsulated in the Source class.
• It fetches OHLCV (Open, High, Low, Close, Volume) data for a given stock symbol across various time intervals (1m, 5m, 30m, 1d, 1wk, 1mo).
• The Source class also calculates common technical indicators like Moving Averages (MA), MACD (Moving Average Convergence Divergence), and Bollinger Bands (BB) on the raw data.
• Special handling is included for filtering trading hours for Chinese stock markets (symbols ending in .SZ or .SS) for intraday intervals.

What data does PyChanLun create?

PyChanLun generates several layers of processed data based on the original OHLCV information, with the main entry point being the Chan class, which acts as a data retriever.

Data Type	Class/Method	Description
Sources	Chan.get_sources()	Raw OHLCV data with MA, MACD, BB calculated.
Sticks	Chan.get_sticks()	Chan Theory (Chan Lun) stick data (Stick class). Columns: date, top (high) and bottom (low).
Fractals	Chan.get_fractals()	Top fractal and bottom fractal data (Fractal class). Columns: date, top (high) and bottom (low).
Strokes	Chan.get_strokes()	Constructed stroke data (Stroke class). Column: date, stroke.
Segments	Chan.get_segments()	Constructed segment data (Segment class), a higher level of structural grouping. Column: date, segment.
Pivots (Stroke/Segment)	Chan.get_stroke_pivots() Chan.get_segment_pivots()	Identified pivot (Pivot class) data based on the stroke or segment data. Columns: entry, exit, high, low , entry MACD, exit MACD, trend level, and divergence status.
Trends (Stroke/Segment)	Chan.get_stroke_pivot_trends() Chan.get_segment_pivot_trends()	Pivot trend data (Trend class) summarizing the price movement between alternating pivots. Columns: entry, exit, entry price, exit price.
Signals (Stroke/Segment)	Chan.get_stroke_pivot_signals() Chan.get_segment_pivot_signals()	Trading signal data (Signal class) generated based on the pivots and MACD divergence. Columns: date, price and signal.

How to use PyChanLun?

The primary way to interact with PyChanLun is through the Chan class, providing a unified interface to access all generated data for a specific stock symbol.

1. Initialization

Instantiate the Chan object with the stock symbol and source data you want to analyze. It will trigger structural analysis across all time intervals of the source data (1m, 5m, 30m, 1d, 1wk, 1mo).

from pychanlun.chan import Chan

# Initialize the Chan object for Apple (AAPL)
chan = Chan('AAPL', source)

2. Accessing Data

Use the various get_* methods, specifying the desired time interval as a string argument (e.g., 1m, 1d). The methods return a Pandas DataFrame containing the analyzed data.

# Get the Stick data for the 1-minute interval
sticks_df = chan.get_sticks('1m')
print("Sticks Data (1m):\n", sticks_df.head())

# Get the Stroke Pivot data for the 30-minute interval
stroke_pivots_df = chan.get_stroke_pivots('30m')
print("\nStroke Pivots (30m):\n", stroke_pivots_df.head())

# Get Segment-level trading signals for the 1-day interval
segment_signals_df = chan.get_segment_pivot_signals('1d')
print("\nSegment Signals (1d):\n", segment_signals_df.head())