xnoapi 0.1.17

XNO API Library for Financial Data

XNO API Library

XNO API is a Python package for retrieving financial data and performing quantitative analysis, specifically optimized for the Vietnamese financial market. It provides a clean, modular interface to access data on stocks, derivatives, and backtesting tools for PnL and performance metrics.

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📌 Key Features

• 🔎 Simple interface to retrieve real-time and historical data for Vietnamese stocks and derivatives
• 📈 Built-in support for performance metrics: Sharpe, Sortino, Max Drawdown, and more
• 📊 Optimized PnL backtesting tools for derivatives with Vietnam-specific fee structures
• 🧪 Compatible with pandas, NumPy for custom strategies and analysis
• 🖼️ Easily extensible for visual output of strategies and metrics

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📦 Installation

Install via pip:

pip install xnoapi

Or clone this repo:

git clone https://github.com/xnoproject/xnoapi.git
pip install ./xnoapi

After installation:

from xnoapi import client
from xnoapi.vn.data import stocks, derivatives
from xnoapi.vn.metrics import Metrics, Backtest_Derivates

client(apikey="your_api_key")

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📚 Documentation

• Online Docs: https://xnoapi.readthedocs.io
• 
• 📄 PDF version

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🚀 Usage Example

Retrieve and analyze Vietnamese stock & derivative data:

from xnoapi import client
from xnoapi.vn.data import stocks, derivatives

client(apikey="your_api_key")

# List of liquid stocks
stocks.list_liquid_asset()

# Historical data for VIC (Vingroup)
vic = stocks.get_hist("VIC")

# Historical data for VN30F1M derivative
vn30f1m = derivatives.get_hist("VN30F1M", "1m")

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🧠 Available Modules

📊 Financial Data

• xnoapi.vn.data.stocks

  • list_liquid_asset(): List of high-liquidity Vietnamese stocks.
  • get_hist(asset): Historical OHLCV data.

• xnoapi.vn.data.derivatives

  • get_hist(asset, frequency): Derivative market data (e.g., VN30F1M).

📈 Metrics & Analytics

• xnoapi.vn.metrics.Metrics:
  • Includes: Sharpe Ratio, Sortino Ratio, Max Drawdown, Avg Gain/Loss, Hit Ratio...
• xnoapi.vn.metrics.Backtest_Derivates:
  • Backtesting logic for trading strategies with support for fee modeling.
• xnoapi.metrics.single_asset.TradingBacktest:
  • Lightweight backtesting class for trading strategies on derivatives (supports raw and after-fee PnL calculation).
  • Metrics included: Sharpe, Sortino, Calmar, Max Drawdown, Win Rate, Profit Factor, Risk of Ruin, etc.

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🧪 Examples

Strategy Evaluation with Metrics

from xnoapi.vn.metrics import Metrics, Backtest_Derivates
from xnoapi.vn.data import derivatives
import numpy as np

# Generate signal: simple strategy based on 20-period median
def gen_position(df):
    return df.assign(
        position=np.sign(df["Close"] - df["Close"].rolling(20).median())
    )

# Fetch 1-minute historical data
df = derivatives.get_hist("VN30F1M", "1m")
df_pos = gen_position(df)

# Backtest the strategy
backtest = Backtest_Derivates(df_pos, pnl_type="raw")

# Initialize metrics
metrics = Metrics(backtest)

# === Backtest_Derivates Methods ===

# Cumulative PNL
cumulative_pnl = backtest.PNL()

# Daily cumulative PNL
daily_cumulative_pnl = backtest.daily_PNL()

# Estimate Minimum Capital Required
min_capital = backtest.estimate_minimum_capital()

# PNL Percentage
pnl_percentage = backtest.PNL_percentage()

# === Metrics Methods ===

# Average Loss
metrics.avg_loss()

# Average Return
metrics.avg_return()

# Average Win
metrics.avg_win()

# Max Drawdown
metrics.max_drawdown()

# Win Rate
metrics.win_rate()

# Volatility
metrics.volatility()

# Sharpe Ratio
metrics.sharpe()

# Sortino Ratio
metrics.sortino()

# Calmar Ratio
metrics.calmar()

# Profit Factor
metrics.profit_factor()

# Risk of Ruin
metrics.risk_of_ruin()

# Value at Risk
metrics.value_at_risk()

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🤝 Credits

Maintained by the XNO Team.
Special thanks to contributors and financial data providers supporting the Vietnamese retail quant community.

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📄 License

This project is licensed under the MIT License.
See LICENSE for full details.