py-candlestick 0.1.1

A pythonic package for working with market data

📈 Candlestick Library

Candlestick is a high‑performance, strongly‑typed Python library for working with financial chart data — including candlesticks (OHLCV) and indicators. Designed with flexibility and performance in mind, it integrates seamlessly with Pandas, NumPy, and other data‑science tools.

Whether you're building backtesting engines, streaming chart visualizations, or performing market analysis, Candlestick provides a clean, structured, and reliable foundation for managing time‑series financial data.

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🚀 Quick Start

pip install py_candlestick

from py_candlestick import Chart, Candle

candles = [
    Candle(timestamp=1700000000, open=1.2, high=1.3, low=1.1, close=1.25, volume=1500),
    Candle(timestamp=1700000600, open=1.25, high=1.32, low=1.2, close=1.28, volume=2100),
]

chart = Chart(candles)
print(len(chart))               # number of candles
print(chart.to_dataframe())    # convert to Pandas DataFrame

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🔥 Key Features

• ✅ Strongly‑typed Candle and Chart classes
• ✅ Fast NumPy‑backed data operations
• ✅ Easy conversion to/from Pandas DataFrames
• ✅ Built‑in indicator registration
• ✅ CSV and MT5 data import utilities
• ✅ Safe chart updates with sequencing and timestamp validation
• ✅ Convenient Python slicing & iteration

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🕯️ Candle Class

Each candlestick is represented by the immutable Candle class:

Candle(
    timestamp: float,
    open: float,
    high: float,
    low: float,
    close: float,
    volume: float
)

Candle Highlights

• Immutable (frozen=True)
• Time‑aware with date_time property
• Helpers: is_bullish(), is_bearish(), is_undecided()
• Conversions: as_dict(), as_tuple(), from_dict()
• Length (len(candle)) equals high‑low range

Example:

c = Candle(timestamp=1700000000, open=1.2, high=1.3, low=1.1, close=1.25, volume=1000)
print(c.is_bullish())  # True
print(c.range_())      # 0.2

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📊 Chart Class

Chart stores a sequence of candles and optional computed indicators.

Creating a Chart

chart = Chart(candles)

Converting to DataFrame

df = chart.to_dataframe(include_timeframe=True, include_symbol=True)

Adding Indicators

Indicators are expected to return an Indicator object with name and NumPy values.

def sma_indicator(candles):
    closes = [c.close for c in candles]
    values = np.convolve(closes, np.ones(5)/5, mode='valid')
    return Indicator(name="sma5", values=values)

chart = Chart(candles, indicator_calculators=[sma_indicator])

Chart Updating

update_chart() adds only new candles (based on timestamp).

chart.update_chart(new_candles)

If an update handler is passed:

def on_update(c):
    print("Chart updated!", len(c))

chart = Chart(candles, on_chart_update=on_update)
chart.update_chart(new_candles)

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📥 Importing Market Data

From CSV

chart = Chart.from_csv("data.csv")

CSV must contain:

timestamp,open,high,low,close,volume

From Pandas DataFrame

chart = Chart.from_pd_dataframe(df)

From MT5 Raw Data

chart = Chart.from_mt5_data(mt5_array, symbol=my_symbol, timeframe=my_tf)

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📦 NumPy Integration

Convert to a stacked matrix of series:

arr = chart.to_ndarray()

Output shape:

(num_candles, num_features)

Where features = timestamp, open, high, low, close, volume, + indicators.

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🧩 Slicing & Iteration

first_10 = chart[:10]
last_candle = chart[-1]
for c in chart:
    print(c.close)

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🤝 Contributing

Pull requests, issues, and improvements are welcome! The project is designed to be clean, readable, and extensible.

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📄 License

MIT License.