openstoxlify 0.2.16

A lightweight Python library for algorithmic trading and market analysis

OpenStoxlify 📈

A lightweight Python library for algorithmic trading and market analysis with professional-grade visualizations.

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✨ Key Features

• Multi-source data: Fetch from Yahoo Finance, Binance, and more
• Strategy engine: Record and visualize trading signals
• Professional charts: OHLC candles, indicators, and strategy markers
• Flexible outputs: Interactive plots and JSON for programmatic use

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🚀 Quick Start

from openstoxlify import fetch, plot, draw
from openstoxlify.models import Period, Provider

# 1. Get market data
data = fetch("AAPL", Provider.YFinance, Period.DAILY)

# 2. Plot closing prices
for quote in data.quotes:
    plot(PlotType.LINE, "Close", quote.timestamp, quote.close)

# 3. Visualize
draw()

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📦 Installation

Basic Installation

pip install openstoxlify

For Development

git clone https://github.com/michaelahli/openstoxlify.git
cd openstoxlify
make clean setup
source venv/bin/activate
python examples/moving_average.py

Requirements

Package	Minimum Version	Notes
Python	3.8+
requests	2.25+	For API communication
matplotlib	3.5+	Only required for visualization
pandas	1.3+	Recommended for data analysis

Troubleshooting

1. Missing Dependencies:

   pip install --upgrade requests matplotlib pandas
   

2. Permission Issues (Linux/Mac):

   pip install --user openstoxlify
   

3. Conda Users:

   conda install -c conda-forge requests matplotlib
   pip install openstoxlify
   

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📊 Core Components

Market Data Fetching

data = fetch(
    symbol="BTC-USD",
    provider=Provider.Binance,
    period=Period.HOURLY
)

Supported Providers:

• YFinance - Yahoo Finance market data
• Binance - Crypto data from Binance

Available Timeframes:

Period	Interval
MINUTELY	1m
QUINTLY	5m
HALFHOURLY	30m
HOURLY	60m
DAILY	1d
WEEKLY	1w
MONTHLY	1mo

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Strategy Signals

act(
    action=ActionType.LONG,      # Trading decision
    timestamp=datetime.now(),    # Exact signal time  
    amount=1000                  # Position size
)

Action Types:

Type	Description	Visual Marker
LONG	Buy/Bullish position	▲ Blue
SHORT	Sell/Bearish position	▼ Purple
HOLD	No action	-

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Visualization

plot(
    graph_type=PlotType.LINE,
    label="RSI(14)", 
    timestamp=quote.timestamp,
    value=65.7,
    screen_index=0
)

Plot Types:

• LINE: Continuous trend lines
• HISTOGRAM: Volume/indicator bars
• AREA: Filled regions (e.g., Bollinger Bands)

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🎨 Visualization with draw()

The draw() function generates professional financial charts combining:

• Price data (OHLC candles)
• Technical indicators
• Trading signals
• Custom annotations

Basic Usage

from openstoxlify import draw

# After plotting data and strategy signals:
draw()  # Displays interactive matplotlib chart

Full Customization Example

draw(
    show_legend=True,             # Toggle legend visibility
    figsize=(16, 9),              # Larger figure size
    offset_multiplier=0.03,       # Adjust trade marker positions
    rotation=45,                  # X-axis label rotation
    title="Custom Strategy Backtest",
    xlabel="Trading Days",
    ylabel="Price (USD)",
    candle_linewidth=0.8,         # Wick thickness
    candle_body_width=3,          # Body thickness
    marker_size=10,               # Trade signal markers
    annotation_fontsize=8,        # Trade annotation text
    histogram_alpha=0.7,          # Histogram transparency
    area_alpha=0.4,               # Area plot transparency
    line_width=2.5                # Trend line thickness
)

### Chart Features

| Element          | Description                         | Example Visual    |
| ---------------- | ----------------------------------- | ----------------- |
| **Candlesticks** | Green/red based on price direction  | 🟩🟥                |
| **Signals**      | Annotated markers for trades        | ▲ LONG<br>▼ SHORT |
| **Indicators**   | Lines, histograms, and filled areas | ─────             |

### Customization Options

```python
# Configure before calling draw()
plt.rcParams.update({
    'figure.figsize': (14, 7),       # Larger canvas
    'lines.linewidth': 2,            # Thicker trend lines
    'font.size': 10                  # Larger annotations
})

draw()  # Now with custom styling

Example Output

(Actual chart would show:)

• Price candles with volume
• SMA lines in different colors
• Triangle markers for entry/exit points
• Strategy annotations with position sizes

Advanced Features

1. Multi-panel Layouts:

# Create subplots
fig, (ax1, ax2) = plt.subplots(2, 1, height_ratios=[3,1])

# Price chart
draw(ax=ax1)  

# Volume histogram 
plot_volume(ax=ax2)  # Your custom function
plt.show()

2. Save to File:

draw()
plt.savefig('strategy_backtest.png', dpi=300)

Key Parameters

Parameter	Type	Default	Description
show_legend	bool	True	Show/hide chart legend
figsize	tuple	(12,6)	Figure dimensions (width, height)
offset_multiplier	float	0.05	Trade marker offset from price
rotation	int	30	X-axis label rotation angle
ha	str	'right'	X-axis label horizontal alignment
title	str	"Market Data Visualizations"	Chart title
xlabel	str	"Date"	X-axis label
ylabel	str	"Price"	Y-axis label
candle_linewidth	float	1	Candlestick wick line width
candle_body_width	float	4	Candlestick body line width
marker_size	int	8	Trade marker size
annotation_fontsize	int	9	Trade annotation font size
histogram_alpha	float	0.6	Histogram bar transparency
area_alpha	float	0.3	Area plot transparency
line_width	float	2	Line plot width

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📝 API Reference

Data Structure

@dataclass
class MarketData:
    ticker: str         # "AAPL"
    period: Period      # Period.DAILY
    provider: Provider  # Provider.YFinance
    quotes: list[Quote] # OHLCV data

Quote Object:

@dataclass
class Quote:
    timestamp: datetime  # Time of measurement
    open: float          # Opening price
    high: float          # Daily high  
    low: float           # Daily low
    close: float         # Closing price
    volume: float        # Trading volume

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📚 Example Strategies

Moving Average Crossover

def ma_crossover(symbol: str, fast: int, slow: int):
    data = fetch(symbol, Period.DAILY)
    closes = [q.close for q in data.quotes]
    
    for i in range(slow, len(closes)):
        fast_ma = sum(closes[i-fast:i])/fast
        slow_ma = sum(closes[i-slow:i])/slow
        
        if fast_ma > slow_ma:
            act(ActionType.LONG, data.quotes[i].timestamp)
        else:
            act(ActionType.SHORT, data.quotes[i].timestamp)

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💡 Tips

1. Use output() to export results for backtesting
2. Combine multiple plot types for rich analysis
3. All timestamps are timezone-aware UTC

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📜 License

MIT © 2025 OpenStoxlify