pypsx-toolkit — Pakistan Stock Exchange Data Library

A clean, simple Python library to fetch and analyze Pakistan Stock Exchange (PSX) market data. Get real-time market data, historical prices, and powerful analysis tools all in one package — no authentication required.

Installation

pip install pypsx-toolkit

Try it in a notebook

Quick Start

Basic Usage - Get Stock Information

import pypsx_toolkit

# Create a ticker object for any stock symbol
ticker = pypsx_toolkit.PSXTicker("OGDC")  # or use pypsx_toolkit.Ticker("OGDC")

# Get company information
info = ticker.info
print(f"Company: {info.get('Sector')}")
print(f"Current Price: {info.get('Current')}")

# Get comprehensive snapshot data (OHLCV, bid/ask, circuit breaker, ranges, ratios, etc.)
snapshot = ticker.snapshot
print(f"Open: {snapshot.get('REG', {}).get('Open')}")
print(f"High: {snapshot.get('REG', {}).get('High')}")
print(f"52-Week Range: {snapshot.get('REG', {}).get('52-WEEK RANGE ^')}")
print(f"P/E Ratio: {snapshot.get('REG', {}).get('P/E Ratio (TTM) **')}")

# Get market watch data for this stock
market_data = ticker.market_watch()
print(market_data)

# Get historical price data (1 year)
history = ticker.history(period="1y", interval="1d")
print(history.head())

# Get recent intraday trades (last 2 days)
intraday = ticker.intraday()
print(intraday.head())

Market Data

import pypsx_toolkit

# Get full market watch (all stocks)
market_watch = pypsx_toolkit.market_watch()
print(f"Total stocks in market watch: {len(market_watch)}")

# Get top performers
performers = pypsx_toolkit.top_performers()
print("Top Gainers:")
print(performers["top_gainers"].head())

print("Top Decliners:")
print(performers["top_decliners"].head())

print("Most Active:")
print(performers["top_actives"].head())

# Get sector summary
sectors = pypsx_toolkit.sector_summary()
print(sectors.head())

# Get all available stock symbols
# get_symbols() returns a list of clean symbols (without suffixes XD, NC, XR)
symbols_list = pypsx_toolkit.get_symbols()
print(f"Total symbols: {len(symbols_list)}")

Historical Data

import pypsx_toolkit

# Get 1 year of historical data for a stock
ticker = pypsx_toolkit.PSXTicker("OGDC")
history = ticker.history(period="1y", interval="1d")

# Get full OHLCV data for specific date range
full_data = ticker.get_historical(start_date="2024-01-01", end_date="2024-12-31")
print(full_data.head())

# Download multiple symbols at once
df = pypsx_toolkit.download(["OGDC", "PPL", "KEL"], period="6mo", interval="1d")
print(df.head())

Indices and Sectors

import pypsx_toolkit

# Get all indices overview
indices = pypsx_toolkit.get_indices()
print(indices.head())

# Get constituents of an index (e.g., KSE100)
kse100 = pypsx_toolkit.index_constituents("KSE100")
print(f"KSE100 has {len(kse100)} constituents")
print(kse100.head())

# Get sector information
sectors = pypsx_toolkit.sector_summary()
print(sectors.head())

# Get complete indices breakdown with statistics
indices_breakdown = pypsx_toolkit.get_indices_breakdown()
print(f"Total indices: {indices_breakdown['total_indices']}")
print(f"Total unique symbols: {indices_breakdown['unique_symbols']}")
for idx, count, stats in indices_breakdown['indices'][:5]:
    print(f"{idx}: {count} symbols (Current: {stats.get('current', 'N/A')})")

# Get complete sector breakdown with company counts and averages
sector_breakdown = pypsx_toolkit.get_sector_breakdown()
print(f"\nTotal sectors: {sector_breakdown['total_sectors']}")
print(f"Total companies: {sector_breakdown['total_companies']}")
for sector in sector_breakdown['sectors'][:5]:
    name = sector['name']
    count = sector['company_count']
    avg_price = sector['averages'].get('current', 'N/A')
    print(f"{name}: {count} companies (Avg Price: {avg_price})")

Main Features

1. Stock Information (PSXTicker)

Create a ticker object for any stock symbol:

ticker = pypsx_toolkit.PSXTicker("OGDC")

Available Properties:

• ticker.info - Get company information (price, sector, volume, etc.)
• ticker.snapshot - Get comprehensive snapshot data from all tabs (OHLCV, bid/ask, circuit breaker, ranges, ratios, etc.)

Available Methods:

• ticker.market_watch() - Get current market watch row for this stock
• ticker.sector() - Get sector-level information
• ticker.history(period="1y", interval="1d") - Get historical data
• ticker.intraday() - Get intraday trades (last ~2 days)
• ticker.get_historical(start_date, end_date) - Get full OHLCV data for date range
• ticker.dividends() - Get dividend information
• ticker.announcements() - Get company announcements
• ticker.orderbook() - Get trading board data (bid/ask prices)

2. Market Data Functions

# Full market watch
market_watch = pypsx_toolkit.market_watch()

# Top performers
performers = pypsx_toolkit.top_performers()  # Returns dict with "top_gainers", "top_decliners", "top_actives"

# Sector summary
sectors = pypsx_toolkit.sector_summary()

# Trading board (order book)
orderbook = pypsx_toolkit.trading_board()

# Get detailed quote data for a symbol (OHLCV, bid/ask, PE ratio, 52-week range, etc.)
quote = pypsx_toolkit.get_quote("OGDC")
print(quote)

# Get quotes for multiple symbols
quotes = pypsx_toolkit.get_quote_batch(["OGDC", "PPL", "KEL"])
for symbol, quote_df in quotes.items():
    if quote_df is not None:
        print(f"{symbol}: {quote_df}")

# Get company fundamentals (business description, financials, ratios, equity profile)
fundamentals = pypsx_toolkit.get_company_fundamentals("OGDC")
print(fundamentals.head())
# Returns DataFrame with CATEGORY, METRIC, VALUE columns
# Categories include: Profile, Governance, Financials Annual, Financials Quarterly, Ratios, Equity Profile

# Get all symbols
symbols = pypsx_toolkit.get_symbols()

3. Batch Downloads

# Download multiple symbols at once
df = pypsx_toolkit.download(["OGDC", "PPL", "KEL"], period="1y", interval="1d")
print(df.head())

# Get intraday data for multiple symbols
intraday_multi = pypsx_toolkit.get_intraday_multiple(["OGDC", "PPL"])
print(intraday_multi.head())

Charting

The library doesn't ship built-in chart helpers — plot any DataFrame it returns directly with matplotlib:

import matplotlib.pyplot as plt
import pypsx_toolkit

df = pypsx_toolkit.PSXTicker("OGDC").history(period="1y", interval="1d")

fig, ax = plt.subplots(figsize=(11, 4))
ax.plot(df.index, df["CLOSE"], color="tab:blue", linewidth=1)
ax.set_title("OGDC — 1Y Close Price")
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Analysis and Statistics

PyPSX Toolkit includes comprehensive analysis tools for stock data, available from pypsx_toolkit.analysis.

Statistical Analysis

from pypsx_toolkit.analysis import returns, volatility, correlation, sharpe_ratio

ticker = pypsx_toolkit.PSXTicker("OGDC")
df = ticker.history(period="1y")

# Calculate returns
rets = returns(df)
print(rets.head())

# Calculate volatility
vol = volatility(df)
print(vol.head())

# Calculate Sharpe ratio
sharpe = sharpe_ratio(df)
print(f"Sharpe Ratio: {sharpe:.3f}")

Technical Indicators

from pypsx_toolkit.analysis import moving_average, rsi, macd, bollinger_bands, exponential_moving_average

ticker = pypsx_toolkit.PSXTicker("OGDC")
df = ticker.history(period="1y")

# Moving averages
df['SMA20'] = moving_average(df, window=20)
df['EMA12'] = exponential_moving_average(df, window=12)

# RSI (supports both 'period' and 'window' parameter names)
df['RSI'] = rsi(df, period=14)

# MACD
macd_line, signal, histogram = macd(df)
df['MACD'] = macd_line
df['Signal'] = signal

# Bollinger Bands
ma, upper, lower = bollinger_bands(df, window=20)
df['BB_Upper'] = upper
df['BB_Lower'] = lower

Automated Insights

from pypsx_toolkit.analysis import interpret_stock, quick_analysis

ticker = pypsx_toolkit.PSXTicker("OGDC")
df = ticker.history(period="1y")

# Generate comprehensive insights
insights = interpret_stock(df, "OGDC")
print("Insights:")
for insight in insights['insights']:
    print(f"  - {insight}")

# Quick analysis
analysis = quick_analysis(df, "OGDC")
print(f"Trading Signal: {analysis['trading_signal']}")
print(f"Sharpe Ratio: {analysis['key_metrics']['sharpe_ratio']:.3f}")
print(f"Max Drawdown: {analysis['key_metrics']['max_drawdown']:.3f}")
print(f"Total Return: {analysis['key_metrics']['total_return']:.2%}")

# Get trading signals directly
from pypsx_toolkit.analysis import generate_trading_signals
signals = generate_trading_signals(df, "OGDC")
print(f"Primary Signal: {signals['primary_signal']}")
print(f"Confidence: {signals['confidence']:.2%}")

Available Analysis Functions:

All analysis functions are available from pypsx_toolkit.analysis:

• Statistics: returns(), volatility(), correlation(), beta(), correlation_matrix()
• Indicators: moving_average(), rsi(), macd(), bollinger_bands(), stochastic(), williams_r(), atr(), adx(), cci(), obv(), vwap()
• Performance: sharpe_ratio(), sortino_ratio(), calmar_ratio(), drawdown(), max_drawdown(), information_ratio(), treynor_ratio()
• Insights: interpret_stock(), quick_analysis(), portfolio_analysis(), market_sentiment_analysis(), generate_trading_signals()

Import them like: from pypsx_toolkit.analysis import sharpe_ratio, rsi

Market Analysis and Breakdowns

Indices Breakdown

Get a comprehensive breakdown of all PSX indices with constituent counts and statistics:

import pypsx_toolkit

# Get indices breakdown
breakdown = pypsx_toolkit.get_indices_breakdown()

print(f"Total Indices: {breakdown['total_indices']}")
print(f"Total Symbols Analyzed: {breakdown['total_symbols_analyzed']}")
print(f"Unique Symbols: {breakdown['unique_symbols']}")

# Print breakdown
for idx, count, stats in breakdown['indices']:
    current = stats.get('current', 'N/A')
    change_pct = stats.get('percentage_change', 'N/A')
    print(f"{idx}: {count} symbols (Current: {current}, Change: {change_pct}%)")

Output includes:

• Total number of indices
• Constituent count for each index
• Index statistics (Current value, Change, Change %)
• Total symbols analyzed (with duplicates across indices)
• Unique symbols across all indices

Sector Breakdown

Get a comprehensive breakdown of all PSX sectors with company counts and computed averages:

import pypsx_toolkit

# Get sector breakdown
breakdown = pypsx_toolkit.get_sector_breakdown()

print(f"Total Sectors: {breakdown['total_sectors']}")
print(f"Total Companies: {breakdown['total_companies']}")

# Print breakdown
for sector in breakdown['sectors'][:10]:  # Top 10 sectors
    name = sector['name']
    count = sector['company_count']
    code = sector['code']
    avg_price = sector['averages'].get('current', 0)
    avg_change = sector['averages'].get('change_%', 0)
    advances = sector['advances']
    declines = sector['declines']

    print(f"{name} (Code: {code}):")
    print(f"  Companies: {count}")
    print(f"  Avg Price: {avg_price:.2f}")
    print(f"  Avg Change %: {avg_change:.2f}%")
    print(f"  Advances: {advances}, Declines: {declines}")
    print()

Output includes:

• Total number of sectors
• Company count per sector
• Average prices, volumes, changes per sector
• Sector-level statistics (advances, declines, turnover)
• Total companies across all sectors

Advanced Usage

Company Information

import pypsx_toolkit

ticker = pypsx_toolkit.PSXTicker("OGDC")

# Get detailed quote data (includes OHLCV, bid/ask prices, PE ratio, 52-week range, VAR, etc.)
quote = pypsx_toolkit.get_quote("OGDC")
print(quote)
# Output includes: OPEN, HIGH, LOW, VOLUME, BID_PRICE, ASK_PRICE, PE_RATIO, VAR, HAIRCUT, etc.

# Get quotes for multiple symbols
quotes = pypsx_toolkit.get_quote_batch(["OGDC", "PPL", "KEL"])
for symbol, quote_df in quotes.items():
    if quote_df is not None:
        print(f"{symbol} Quote:")
        print(quote_df)

# Get company fundamentals (business description, financials, ratios, equity profile)
fundamentals = pypsx_toolkit.get_company_fundamentals("OGDC")
print(fundamentals.head())

# Get comprehensive snapshot data from all tabs (most holistic approach)
snapshot = pypsx_toolkit.get_snapshot("BOP")
print(snapshot['REG'])  # REG tab contains: OHLCV, circuit breaker, ranges, bid/ask, ratios, etc.
# Or use ticker.snapshot property:
ticker = pypsx_toolkit.PSXTicker("BOP")
snap = ticker.snapshot
print(f"Open: {snap['REG']['Open']}")
print(f"52-Week Range: {snap['REG']['52-WEEK RANGE ^']}")

# Get announcements
announcements = ticker.announcements()
print(announcements.head())

# Get dividends
dividends = ticker.dividends()
print(dividends)

# Get order book
orderbook = ticker.orderbook()
print(orderbook)

Custom Date Ranges

ticker = pypsx_toolkit.PSXTicker("OGDC")

# Get historical data for specific date range
historical = ticker.get_historical(
    start_date="2024-01-01",
    end_date="2024-12-31"
)
print(historical.head())

Examples

Example 1: Find Top Volume Stocks

import pypsx_toolkit

# Get market watch
mw = pypsx_toolkit.market_watch()

# Sort by volume and get top 5
top_volume = mw.nlargest(5, "Volume")[['Current', 'Change', 'Volume']]
print(top_volume)

Example 2: Compare Stock Performance

import pypsx_toolkit
import matplotlib.pyplot as plt

symbols = ["OGDC", "PPL", "KEL"]
fig, ax = plt.subplots(figsize=(11, 4))
for symbol in symbols:
    df = pypsx_toolkit.PSXTicker(symbol).history(period="6mo", interval="1d")
    ax.plot(df.index, df["CLOSE"] / df["CLOSE"].iloc[0], label=symbol)

ax.set_title("Normalized Close Price — 6 Months")
ax.legend()
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Example 3: Technical Analysis

import pypsx_toolkit
from pypsx_toolkit.analysis import rsi, macd, bollinger_bands

ticker = pypsx_toolkit.PSXTicker("OGDC")
df = ticker.history(period="1y")

# Add technical indicators
df['RSI'] = rsi(df, period=14)
macd_line, signal, _ = macd(df)
df['MACD'] = macd_line
df['Signal'] = signal

ma, upper, lower = bollinger_bands(df)
df['BB_Upper'] = upper
df['BB_Lower'] = lower

# Simple trading signal (SMA crossover)
df['SMA20'] = df['CLOSE'].rolling(20).mean()
df['SMA50'] = df['CLOSE'].rolling(50).mean()
df['Signal'] = (df['SMA20'] > df['SMA50']).astype(int)

print(df[['CLOSE', 'RSI', 'MACD', 'Signal']].tail())

Example 4: Portfolio Analysis

import pypsx_toolkit
from pypsx_toolkit.analysis import portfolio_analysis

# Create a portfolio
portfolio = {
    "OGDC": pypsx_toolkit.PSXTicker("OGDC").history(period="1y"),
    "PPL": pypsx_toolkit.PSXTicker("PPL").history(period="1y"),
    "KEL": pypsx_toolkit.PSXTicker("KEL").history(period="1y"),
}

# Analyze portfolio
analysis = portfolio_analysis(portfolio)
print(f"Avg Correlation: {analysis['portfolio_metrics']['avg_correlation']:.3f}")
print(f"Avg Volatility: {analysis['portfolio_metrics']['avg_volatility']:.3f}")
print(f"Best Performer: {analysis['portfolio_metrics']['best_performer']}")
print(f"Market Sentiment: {analysis['market_sentiment']} ({analysis['sentiment_strength']})")
for insight in analysis["portfolio_insights"]:
    print(f"  - {insight}")

API Reference

PSXTicker Class

import pypsx_toolkit

ticker = pypsx_toolkit.PSXTicker("OGDC")

# Properties (no parentheses)
ticker.info                    # Dict with company info
ticker.snapshot                # Dict with comprehensive snapshot data from all tabs
ticker.fast_info               # Quick metrics dict

# Methods
ticker.history(period="1y", interval="1d")            # Historical data
ticker.intraday()                                     # Intraday trades
ticker.get_historical("2024-01-01", "2024-12-31")     # Full OHLCV data
ticker.market_watch()                                 # Market watch row
ticker.sector()                                       # Sector information
ticker.orderbook()                                    # Trading board data
ticker.dividends()                                    # DataFrame with dividends
ticker.announcements()                                # DataFrame with announcements

Market Functions

pypsx_toolkit.market_watch()           # Full market watch DataFrame
pypsx_toolkit.top_performers()         # Dict: {top_gainers, top_decliners, top_actives}
pypsx_toolkit.sector_summary()         # Sector summary DataFrame
pypsx_toolkit.get_indices()            # Indices overview DataFrame
pypsx_toolkit.get_indices_breakdown() # Complete indices breakdown with counts and stats
pypsx_toolkit.get_sector_breakdown()  # Complete sector breakdown with company counts and averages
pypsx_toolkit.get_symbols()            # List of all stock symbols
pypsx_toolkit.trading_board()          # Trading board DataFrame

# Quote functions - Get detailed quote data (OHLCV, bid/ask, PE ratio, 52-week range, etc.)
pypsx_toolkit.get_quote(symbol)                  # Get detailed quote for a single symbol
pypsx_toolkit.get_quote_batch(symbols)           # Get quotes for multiple symbols (returns dict)

# Company fundamentals - Get comprehensive company data (business description, financials, ratios, etc.)
pypsx_toolkit.get_company_fundamentals(symbol)   # Get company fundamentals (returns DataFrame)

# Snapshot - Get comprehensive snapshot data from all tabs (most holistic approach)
pypsx_toolkit.get_snapshot(symbol)              # Get snapshot data from all tabs (returns dict with tab names as keys)
# Or use ticker.snapshot property for easier access

Download Functions

import pypsx_toolkit

symbols = ["OGDC", "HBL"]

pypsx_toolkit.download(symbols, period="1y", interval="1d")         # Batch download
pypsx_toolkit.get_intraday_multiple(symbols)                        # Multiple intraday
pypsx_toolkit.get_historical("OGDC", "2024-01-01", "2024-12-31")    # Historical OHLCV

Backward Compatibility

The library maintains backward compatibility:

• pypsx_toolkit.Ticker is an alias for pypsx_toolkit.PSXTicker
• pypsx_toolkit.PSXSymbol is also available (legacy)

Notes

• Dividends: dividend records use their own column naming, which differs from the conventions used elsewhere in the toolkit.
• Data Availability: Some data may not be available when the market is closed.
• Symbol Names: Use official PSX symbols (e.g., "OGDC", "PPL", "KEL").

License

Proprietary — All rights reserved. Unauthorized use, copying, or distribution is prohibited.