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New Features Implementation

This document describes the new features added to pyPortMan.

Features Implemented

1. Multi-Broker Support (multi_broker_support.py)

Support for 5Paisa, Upstox, and Dhan brokers with unified portfolio view.

Supported Brokers

  • 5Paisa - Full API integration
  • Upstox - OAuth-based authentication
  • Dhan - REST API support

Key Features

  • Unified portfolio view across all brokers
  • Broker arbitrage opportunities detection
  • Cross-broker order placement
  • Consolidated holdings, positions, and orders

Usage Example

from multi_broker_support import UnifiedPortfolioManager, create_broker

# Create portfolio manager
manager = UnifiedPortfolioManager()

# Add brokers
broker1 = create_broker("5paisa", api_key="xxx", api_secret="xxx",
                       user_id="xxx", password="xxx", client_id="xxx")
broker2 = create_broker("upstox", api_key="xxx", api_secret="xxx",
                       user_id="xxx", password="xxx")

manager.add_broker("5paisa", broker1)
manager.add_broker("upstox", broker2)

# Login to all brokers
manager.login_all()

# Get unified holdings
holdings = manager.get_unified_holdings()

# Find arbitrage opportunities
arbitrage = manager.find_arbitrage_opportunities(["RELIANCE", "TCS"])

# Export portfolio
filename = manager.export_portfolio(format="excel")

2. Market Data Integration (market_data_integration.py)

NSE/BSE live data feeds, option chain, F&O data, and corporate actions tracking.

Data Providers

  • NSE - Official NSE API
  • BSE - BSE API integration

Key Features

  • Real-time quotes
  • Historical data (OHLCV)
  • Option chain with Greeks
  • F&O data
  • Corporate actions
  • Market movers (gainers/losers)
  • Index data
  • Put-Call Ratio (PCR)
  • Max Pain calculation

Usage Example

from market_data_integration import MarketDataManager

# Create data manager
manager = MarketDataManager()

# Get quote
quote = manager.get_quote("RELIANCE", exchange="NSE")

# Get historical data
historical = manager.get_historical_data("RELIANCE", interval="day",
                                        from_date="01-01-2024",
                                        to_date="31-12-2024")

# Get option chain
options = manager.get_option_chain("NIFTY", expiry="2024-12-26")

# Get F&O data
fo_data = manager.get_fo_data("RELIANCE")

# Get corporate actions
actions = manager.get_corporate_actions("RELIANCE")

# Get market movers
movers = manager.get_market_movers(top_n=10)

3. Advanced Charting (advanced_charting.py)

Candlestick charts with technical indicators and multi-stock comparison.

Technical Indicators

  • Moving Averages: SMA, EMA
  • Momentum: RSI, Stochastic, Williams %R
  • Trend: MACD, ADX, Supertrend
  • Volatility: Bollinger Bands, ATR
  • Volume: OBV, VWAP
  • Custom: Ichimoku Cloud, Heikin Ashi

Key Features

  • Candlestick charts
  • Multiple indicators overlay
  • Multi-stock comparison
  • Correlation heatmap
  • Performance summary
  • Custom indicator builder

Usage Example

from advanced_charting import CandlestickChart, MultiStockComparison

# Create candlestick chart
chart = CandlestickChart(figsize=(14, 10))
fig = chart.create_chart(data, indicators=['sma_20', 'sma_50', 'rsi', 'macd', 'bollinger'])
chart.save_chart("chart.png")

# Multi-stock comparison
comparison = MultiStockComparison()
fig = comparison.compare_stocks({
    "RELIANCE": reliance_data,
    "TCS": tcs_data,
    "INFY": infy_data
})

# Correlation heatmap
heatmap = comparison.correlation_heatmap(stocks_data)

# Performance summary
summary = comparison.performance_summary(stocks_data)

4. Tax Reporting (tax_reporting.py)

Capital gains calculation (STCG/LTCG), tax-ready reports, and financial year summaries.

Tax Features

  • FIFO-based capital gains calculation
  • STCG/LTCG classification
  • Indexation benefit for non-equity assets
  • Asset-wise breakdown
  • Symbol-wise breakdown
  • ITR form data generation
  • Excel/JSON export

Tax Rates (FY 2024-25)

  • Equity STCG: 15% (held < 1 year)
  • Equity LTCG: 10% on gains above ₹1 lakh (held ≥ 1 year)
  • Other STCG: As per income slab
  • Other LTCG: 20% with indexation

Usage Example

from tax_reporting import TaxCalculator, TaxReportGenerator, TaxYear

# Create tax calculator
calculator = TaxCalculator()

# Add transactions
calculator.add_transactions_from_dataframe(tradebook_df)

# Calculate capital gains
gains = calculator.calculate_capital_gains(TaxYear.FY_2024_25)

# Get tax summary
summary = calculator.get_tax_summary(TaxYear.FY_2024_25)

# Generate reports
report_gen = TaxReportGenerator(calculator)

# Export to Excel
filename = report_gen.export_to_excel(TaxYear.FY_2024_25)

# Generate ITR form data
itr_data = report_gen.generate_itr_form_data(TaxYear.FY_2024_25)

Installation

Install the required dependencies:

pip install pandas numpy matplotlib requests scipy openpyxl

For Jupyter notebook usage:

pip install jupyter notebook ipywidgets qgrid

File Structure

pyPortMan/
├── multi_broker_support.py      # Multi-broker integration
├── market_data_integration.py   # Market data providers
├── advanced_charting.py         # Charting and indicators
├── tax_reporting.py             # Tax calculation and reports
├── hjOpenTerminal.ipynb         # Main notebook
├── auth_info.xlsx               # Broker credentials
├── stocks.xlsx                  # Stock list
└── README_NEW_FEATURES.md       # This file

API Reference

Multi-Broker Support

Classes

  • BrokerBase - Base broker class
  • FivePaisaBroker - 5Paisa integration
  • UpstoxBroker - Upstox integration
  • DhanBroker - Dhan integration
  • UnifiedPortfolioManager - Portfolio manager

Methods

  • login() - Authenticate with broker
  • get_holdings() - Get holdings
  • get_positions() - Get positions
  • get_orders() - Get orders
  • place_order() - Place order
  • cancel_order() - Cancel order
  • get_quote() - Get quote

Market Data Integration

Classes

  • MarketDataProvider - Base provider class
  • NSEDataProvider - NSE data provider
  • BSEDataProvider - BSE data provider
  • MarketDataManager - Unified data manager

Methods

  • get_quote() - Get live quote
  • get_historical_data() - Get historical data
  • get_option_chain() - Get option chain
  • get_fo_data() - Get F&O data
  • get_corporate_actions() - Get corporate actions
  • get_market_movers() - Get gainers/losers

Advanced Charting

Classes

  • TechnicalIndicators - Indicator calculations
  • CandlestickChart - Chart creation
  • MultiStockComparison - Multi-stock analysis
  • CustomIndicator - Custom indicator builder

Indicators

  • sma() - Simple Moving Average
  • ema() - Exponential Moving Average
  • rsi() - Relative Strength Index
  • macd() - MACD
  • bollinger_bands() - Bollinger Bands
  • stochastic() - Stochastic Oscillator
  • atr() - Average True Range
  • adx() - Average Directional Index

Tax Reporting

Classes

  • TaxCalculator - Tax calculation engine
  • TaxReportGenerator - Report generator
  • TaxYear - Financial year enum
  • TransactionType - Transaction type enum
  • AssetType - Asset type enum

Methods

  • calculate_capital_gains() - Calculate gains
  • get_tax_summary() - Get summary
  • export_to_excel() - Export to Excel
  • export_to_json() - Export to JSON
  • generate_itr_form_data() - Generate ITR data

Examples

Example 1: Complete Workflow

from multi_broker_support import UnifiedPortfolioManager, create_broker
from market_data_integration import MarketDataManager
from advanced_charting import CandlestickChart
from tax_reporting import TaxCalculator, TaxReportGenerator, TaxYear

# 1. Setup multi-broker portfolio
manager = UnifiedPortfolioManager()
broker = create_broker("zerodha", api_key="xxx", api_secret="xxx",
                       user_id="xxx", password="xxx")
manager.add_broker("zerodha", broker)
manager.login_all()

# 2. Get market data
data_manager = MarketDataManager()
quote = data_manager.get_quote("RELIANCE")
historical = data_manager.get_historical_data("RELIANCE")

# 3. Create chart
chart = CandlestickChart()
fig = chart.create_chart(historical, indicators=['sma_20', 'rsi', 'macd'])
chart.save_chart("reliance_chart.png")

# 4. Generate tax report
calculator = TaxCalculator()
calculator.add_transactions_from_dataframe(tradebook)
report_gen = TaxReportGenerator(calculator)
report_gen.export_to_excel(TaxYear.FY_2024_25)

Example 2: Arbitrage Detection

from multi_broker_support import UnifiedPortfolioManager, create_broker

manager = UnifiedPortfolioManager()

# Add multiple brokers
for broker_name in ["zerodha", "5paisa", "upstox"]:
    broker = create_broker(broker_name, **broker_config[broker_name])
    manager.add_broker(broker_name, broker)

manager.login_all()

# Find arbitrage opportunities
symbols = ["RELIANCE", "TCS", "INFY", "HDFC"]
arbitrage = manager.find_arbitrage_opportunities(symbols)

print(arbitrage)

Example 3: Option Chain Analysis

from market_data_integration import MarketDataManager

manager = MarketDataManager()

# Get option chain
options = manager.get_option_chain("NIFTY", expiry="2024-12-26")

# Calculate PCR
from market_data_integration import calculate_pcr
pcr = calculate_pcr(options)

# Calculate Max Pain
from market_data_integration import calculate_max_pain
max_pain = calculate_max_pain(options)

print(f"PCR: {pcr:.2f}")
print(f"Max Pain: {max_pain}")

Notes

  • All broker APIs require valid credentials
  • Market data may have rate limits
  • Tax calculations are based on Indian tax laws for FY 2024-25
  • Charts require matplotlib backend for display

Support

For issues or questions, please refer to the main README or contact the project maintainers.