assetlab 0.1.0

A powerful Python library for financial calculations, including cash flow analysis, asset valuation, and portfolio metrics.

AssetLab

AssetLab is a powerful Python library for financial calculations, including cash flow analysis, asset valuation, and portfolio metrics.

🌍 Multilingual Support: This project supports both English and Russian languages. See README_ru.md for Russian documentation.

🚀 Features

💰 Cash Flow Analysis (CashFlow)

• XNPV - Net Present Value for irregular cash flows
• XIRR - Internal Rate of Return
• MIRR - Modified Internal Rate of Return
• DPP - Discounted Payback Period
• Duration - Macaulay and Modified
• Profitability Index and Payback Period
• Visualization of cash flows
• Export to pandas DataFrame

🏦 Deposit Modeling (Deposit)

• Interest capitalization and payment
• Deposits and partial withdrawals
• Various interest compounding frequencies
• Conversion to CashFlow for analysis

📈 Bond Modeling (Bond)

• Fixed and floating coupons
• Amortization and early redemption
• Support for caps and floors on floating rates
• Functional base rates
• YTM, current yield, duration calculations

📦 Installation

From Source

git clone https://github.com/your-username/AssetLab.git
cd AssetLab
pip install -e .

Dependencies

• Python 3.9+
• pandas >= 1.5.0
• numpy >= 1.23.0
• pydantic >= 2.0.0
• scipy >= 1.9.0
• matplotlib >= 3.7.0

🎯 Quick Start

Bond Investment Analysis

import pandas as pd
from assetlab import CashFlow, Payment

# Create a bond cash flow
bond_cf = CashFlow(payments=[
    Payment(date=pd.Timestamp('2024-01-15'), amount=-980),  # Purchase
    Payment(date=pd.Timestamp('2025-01-15'), amount=60),    # 1st coupon
    Payment(date=pd.Timestamp('2026-01-15'), amount=60),    # 2nd coupon
    Payment(date=pd.Timestamp('2027-01-15'), amount=1060),  # 3rd coupon + redemption
])

# Calculate key metrics
discount_rate = 0.05
npv = bond_cf.xnpv(discount_rate)
irr = bond_cf.xirr()
duration = bond_cf.modified_duration(yield_rate=irr)

print(f"NPV: {npv:.2f}")
print(f"IRR: {irr:.2%}")
print(f"Duration: {duration:.2f} years")

Bank Deposit Modeling

from assetlab import Deposit, Payment

# Create a deposit with compounding
deposit = Deposit(
    principal=100000,
    annual_rate=0.08,
    start_date=pd.Timestamp('2024-01-01'),
    end_date=pd.Timestamp('2026-01-01'),
    interest_frequency=4,  # Quarterly compounding
    replenishments=[
        Payment(date=pd.Timestamp('2024-06-15'), amount=20000)
    ]
)

# Get results
final_value = deposit.final_value()
total_interest = deposit.total_interest()
cashflow = deposit.to_cashflow()

print(f"Final amount: {final_value:.2f}")
print(f"Total interest: {total_interest:.2f}")

📊 Usage Examples

Portfolio Analysis

# Create several assets
bond_cf = CashFlow(payments=[
    Payment(date=pd.Timestamp('2024-01-01'), amount=-1000),
    Payment(date=pd.Timestamp('2025-01-01'), amount=1100),
])

stock_cf = CashFlow(payments=[
    Payment(date=pd.Timestamp('2024-01-01'), amount=-1500),
    Payment(date=pd.Timestamp('2024-06-01'), amount=50),   # Dividend
    Payment(date=pd.Timestamp('2025-01-01'), amount=1600), # Sale
])

# Combine into portfolio
portfolio = bond_cf + stock_cf

# Analyze portfolio
portfolio_npv = portfolio.xnpv(0.05)
portfolio_irr = portfolio.xirr()

print(f"Portfolio NPV: {portfolio_npv:.2f}")
print(f"Portfolio IRR: {portfolio_irr:.2%}")

Cash Flow Visualization

import matplotlib.pyplot as plt

# Create chart
fig, ax = plt.subplots(figsize=(10, 6))
bond_cf.plot(ax=ax, width=20)
ax.set_title("Bond Cash Flow")
plt.show()

📘 Example: Bond Analysis (Bond)

import pandas as pd
from assetlab import Bond, Payment, FloatingRateConfig

# Simple bond with amortization example
bond = Bond(
    issue_date=pd.Timestamp('2024-01-01'),
    maturity_date=pd.Timestamp('2027-01-01'),
    face_value=1000,
    coupon_rate=0.07,  # 7% annual
    coupon_frequency=2,  # Semi-annual coupon
    amortizations=[
        Payment(date=pd.Timestamp('2025-01-01'), amount=200),
        Payment(date=pd.Timestamp('2026-01-01'), amount=200)
    ]
)

# Generate cash flow
cf = bond.to_cashflow()
print(cf.to_dataframe())

# Analyze yield to maturity (YTM) when purchased at 980
ytm = bond.ytm(price=980)
print(f"YTM: {ytm:.2%}")

# Current yield
current_yield = bond.current_yield(price=980)
print(f"Current yield: {current_yield:.2%}")

# Duration
macaulay = bond.macaulay_duration(price=980)
print(f"Macaulay duration: {macaulay:.2f} years")

Floating Rate Bond

# Floating rate configuration
floating_config = FloatingRateConfig(
    base_rate=0.03,  # Base rate 3%
    spread=0.02,     # Spread 2%
    cap=0.08,        # Maximum rate 8%
    floor=0.01       # Minimum rate 1%
)

# Floating rate bond
floating_bond = Bond(
    issue_date=pd.Timestamp('2024-01-01'),
    maturity_date=pd.Timestamp('2029-01-01'),
    face_value=1000,
    coupon_rate=floating_config,
    coupon_frequency=2
)

# Analyze coupon schedule
schedule = floating_bond.get_coupon_schedule()
print("Floating rate bond coupon schedule:")
for item in schedule:
    print(f"Period {item['period']}: Effective rate: {item['effective_rate']:.3%}, "
          f"Coupon: {item['coupon_amount']:.2f}")

Working with Accrued Interest (AI)

# Analysis on arbitrary date
analysis_date = pd.Timestamp('2024-03-15')

# Coupon dates
next_coupon = bond.next_coupon_date(analysis_date)
prev_coupon = bond.previous_coupon_date(analysis_date)
print(f"Next coupon: {next_coupon}")
print(f"Previous coupon: {prev_coupon}")

# Accrued interest
accrued = bond.accrued_interest(analysis_date)
print(f"Accrued interest on {analysis_date.date()}: {accrued:.2f}")

# Working with clean and dirty prices
dirty_price = 1025.83  # Price with AI
clean_price = bond.clean_price(dirty_price, analysis_date)
print(f"Clean price: {clean_price:.2f}")

# Reverse operation
dirty_price_calc = bond.dirty_price(clean_price, analysis_date)
print(f"Dirty price: {dirty_price_calc:.2f}")

# Current yield with AI
current_yield = bond.current_yield(dirty_price, analysis_date)
print(f"Current yield: {current_yield:.2%}")

Analysis on Arbitrary Date

# Bond analysis on current date (not issue date)
current_date = pd.Timestamp('2024-06-15')

# YTM on current date
ytm_current = bond.ytm(price=950, analysis_date=current_date)
print(f"YTM on {current_date.date()}: {ytm_current:.2%}")

# Duration on current date
duration_current = bond.macaulay_duration(price=950, analysis_date=current_date)
print(f"Duration on {current_date.date()}: {duration_current:.2f} years")

# Price from YTM on current date
price_from_ytm = bond.price_from_ytm(ytm=0.06, analysis_date=current_date)
print(f"Price at 6% YTM on {current_date.date()}: {price_from_ytm:.2f}")

⚙️ Settings

The library supports global settings:

from assetlab import settings

# Change day count basis for calculations
settings.settings.DAY_COUNT = 360.0  # Default is 365.0

🧪 Testing

Running tests:

# All tests
pytest

# With code coverage
tox

# Individual module
pytest tests/test_cashflow.py

📈 Code Coverage

Current test coverage: 96%

• assetlab/__init__.py: 100%
• assetlab/cashflow.py: 96%
• assetlab/deposit.py: 94%
• assetlab/bond.py: 97%
• assetlab/settings.py: 100%

📚 Documentation

Jupyter Notebooks

Detailed usage examples are available in the examples/ folder:

• 01_investment_analysis.ipynb - investment and portfolio analysis
• 02_deposit_analysis.ipynb - deposit analysis with replenishments
• 03_bond_analysis.ipynb - bond analysis (fixed, floating, amortization, callable)

Russian versions are available in the examples/ru/ folder:

• 01_investment_analysis.ipynb - investment and portfolio analysis
• 02_deposit_analysis.ipynb - deposit analysis with replenishments
• 03_bond_analysis.ipynb - bond analysis (fixed, floating, amortization, callable)

Sphinx Documentation

Complete API documentation is available in the docs/ folder:

cd docs
make html
open _build/html/index.html

Documentation includes:

• Auto-generated API documentation
• Usage examples
• Detailed description of all classes and methods

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Maxim - [e-mail]

🔗 Links

• Repository
• Issues
• Releases