What is the ORTEX Python SDK?

The official Python SDK provides programmatic access to ORTEX financial market data. Access the same institutional-grade short interest, options analytics, and stock scores used by professional traders — directly in your Python applications, Jupyter notebooks, and automated trading systems.

Installation

pip install ortex

Authentication

Get your API key at app.ortex.com/apis

import ortex

# Option 1: Set API key directly
ortex.set_api_key("your-api-key")

# Option 2: Use environment variable
# export ORTEX_API_KEY="your-api-key"
# The SDK will automatically use this

# Option 3: Pass to each function
response = ortex.get_short_interest("NYSE", "AMC", api_key="your-api-key")

Quick Start

import ortex

ortex.set_api_key("your-api-key")

# Get short interest data
response = ortex.get_short_interest("NYSE", "AMC")
df = response.df  # Access data as DataFrame

# Check your credit usage
print(f"Credits used: {response.credits_used}")
print(f"Credits left: {response.credits_left}")

# Get historical short interest
response = ortex.get_short_interest("NYSE", "AMC", "2024-01-01", "2024-12-31")

# Get stock prices
response = ortex.get_price("NASDAQ", "AAPL", "2024-01-01", "2024-12-31")
df = response.df

Response Object

All functions return an OrtexResponse object:

response = ortex.get_short_interest("NYSE", "AMC")

# Access data as DataFrame
df = response.df

# Access raw row data
rows = response.rows

# Credit tracking
print(f"Credits used: {response.credits_used}")
print(f"Credits left: {response.credits_left}")

# Pagination info
print(f"Total results: {response.length}")
print(f"Has next page: {response.has_next_page}")

# Iterate through all pages
for page in response.iter_all_pages():
    process(page.df)

# Get all data at once (fetches all pages)
full_df = response.to_dataframe_all()

Available Functions

Short Interest

# Short interest data for a stock
response = ortex.get_short_interest("NYSE", "AMC")
response = ortex.get_short_interest("NYSE", "AMC", "2024-01-01", "2024-12-31")

# Short interest for an index (S&P 500, NASDAQ 100, etc.)
response = ortex.get_index_short_interest("US-S 500")

# Share availability for shorting
response = ortex.get_short_availability("NYSE", "AMC")

# Cost to borrow (all loans or new loans)
response = ortex.get_cost_to_borrow("NYSE", "AMC")
response = ortex.get_cost_to_borrow("NYSE", "AMC", loan_type="new")

# Days to cover
response = ortex.get_days_to_cover("NYSE", "AMC")

Stock Prices

# OHLCV price data
response = ortex.get_price("NASDAQ", "AAPL")
response = ortex.get_price("NASDAQ", "AAPL", "2024-01-01", "2024-12-31")

# Close price
response = ortex.get_close_price("NASDAQ", "AAPL")

Shares & Float

# Free float shares (from_date is required)
response = ortex.get_free_float("NYSE", "F", "2024-01-01")

# Shares outstanding
response = ortex.get_shares_outstanding("NYSE", "F", "2024-01-01")

Fundamentals

# Income statement
response = ortex.get_income_statement("NYSE", "F", "2024Q3")
print(f"Company: {response.company}")
print(f"Period: {response.period}")
df = response.df

# Balance sheet
response = ortex.get_balance_sheet("NYSE", "F", "2024Q3")

# Cash flow statement
response = ortex.get_cash_flow("NYSE", "F", "2024Q3")

# Financial ratios
response = ortex.get_financial_ratios("NYSE", "F", "2024Q3")

# Fundamentals summary
response = ortex.get_fundamentals_summary("NYSE", "F", "2024Q3")

# Valuation metrics
response = ortex.get_valuation("NYSE", "F", "2024Q3")

EU Short Interest

# EU short positions (individual holders)
response = ortex.get_eu_short_positions("XETR", "SAP")

# EU short positions at a specific date
response = ortex.get_eu_short_positions("XETR", "SAP", "2024-12-01")

# EU short positions history
response = ortex.get_eu_short_positions_history("XETR", "SAP", "2024-01-01", "2024-12-31")

# Total EU short interest
response = ortex.get_eu_short_total("XETR", "SAP")

Market Data

# Earnings calendar
response = ortex.get_earnings("2024-12-01", "2024-12-31")

# List of exchanges
response = ortex.get_exchanges()
response = ortex.get_exchanges("United States")

# Macro economic events
response = ortex.get_macro_events("US")
response = ortex.get_macro_events("US", "2024-12-01", "2024-12-15")

Pagination

Control page size and iterate through large result sets:

# Set page size
response = ortex.get_short_interest("NYSE", "AMC", page_size=100)

# Iterate through all pages
all_data = []
for page in response.iter_all_pages():
    all_data.extend(page.rows)
    print(f"Fetched {len(page.rows)} rows, credits used: {page.credits_used}")

# Or get everything at once
full_df = response.to_dataframe_all()

Error Handling

The SDK provides specific exception types:

import ortex
from ortex import (
    APIError,
    AuthenticationError,
    RateLimitError,
    NotFoundError,
    ValidationError,
    ServerError,
)

try:
    response = ortex.get_short_interest("NYSE", "INVALID")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after} seconds")
except NotFoundError:
    print("Stock not found")
except ValidationError as e:
    print(f"Invalid parameters: {e}")
except ServerError:
    print("ORTEX server error")
except APIError as e:
    print(f"API error: {e}")

Rate Limiting

The SDK automatically handles rate limiting with exponential backoff:

Automatically retries on 429 (rate limit) responses
Uses exponential backoff (1s, 2s, 4s, 8s, up to 60s)
Maximum 5 retry attempts by default

Configure retry behavior:

from ortex import OrtexClient

client = OrtexClient(
    api_key="your-api-key",
    timeout=60,      # Request timeout in seconds
    max_retries=10,  # Maximum retry attempts
)

Requirements

Python 3.9+
pandas >= 2.0.0
requests >= 2.31.0
tenacity >= 8.2.0

Resources

Frequently Asked Questions

How do I install the ORTEX Python SDK?

Run pip install ortex in your terminal. The SDK requires Python 3.9 or higher.

How do I get an API key for the Python SDK?

Visit app.ortex.com/apis to subscribe and receive your API key instantly.

Does the SDK return pandas DataFrames?

Yes. All responses include a .df property that returns your data as a pandas DataFrame, ready for analysis.

How does pagination work?

Use response.iter_all_pages() to iterate through large datasets, or response.to_dataframe_all() to fetch all pages into a single DataFrame.

Is the SDK rate-limited?

The SDK automatically handles rate limiting with exponential backoff. You can monitor your usage via response.credits_used and response.credits_left.