Tepilora 0.5.4

Python SDK for Tepilora API v3

Tepilora SDK (Python)

Python SDK (sync + async) for Tepilora API v3.

262 operations across 28 namespaces, auto-generated from the registry.

Install

pip install Tepilora

Optional extras:

pip install 'Tepilora[arrow]'   # PyArrow for binary formats
pip install 'Tepilora[polars]'  # Polars DataFrame support

Quick Start

import Tepilora as T

client = T.TepiloraClient(api_key="YOUR_KEY")

# Typed endpoints (IDE autocomplete)
securities = client.securities.search(query="MSCI ETF", limit=10)
print(securities["totalCount"])

# Raw call
resp = client.call("securities.search", params={"query": "MSCI", "limit": 5})
print(resp.data)

Async

import asyncio
import Tepilora as T

async def main():
    async with T.AsyncTepiloraClient(api_key="YOUR_KEY") as client:
        data = await client.securities.search(query="MSCI", limit=10)
        print(data)

asyncio.run(main())

Namespaces

Namespace	Operations	Description
securities	12	Search, filter, history, facets, MiFID, fees
news	7	Search, latest, trending, details
publications	5	Research reports and publications
portfolio	20	CRUD, returns, attribution, optimization
analytics	68	Rolling metrics, ratios, risk, factors
alerts	9	Alert rules CRUD, evaluate, history
macro	6	Economic indicators, calendar
stocks	9	Technicals, screening, peers, signals
bonds	9	Analyze, screen, ladder, curve, spread, lookup
options	6	Pricing, Greeks, IV, strategies
esg	5	ESG screening, comparison, portfolio analytics
factors	7	Fama-French, momentum, factor risk models
evolution	12	Feedback, voting, comments, moderation
reporting	8	Tearsheets, fund sheets, templates, rendering
fh	7	Fundamentals history, financials
clients	8	B2B client management
profiling	10	MiFID questionnaires, suitability
billing	10	Fee calculations, schedules, records
documents	4	Document parsing, classification
alternatives	9	Alternative investments
queries	8	Saved queries CRUD, execute
search	1	Global search
data	1	Raw data access
exports	2	Data export to file formats
asset_allocation	10	Strategic asset allocation, model portfolios
realtime	5	Real-time market data, streaming
workflows	2	Cross-module workflows

Examples by Namespace

Securities

# Search
results = client.securities.search(query="MSCI World", limit=20)

# Get details
details = client.securities.details(identifier="IE00B4L5Y983EURXMIL")

# Price history
history = client.securities.history(
    identifiers=["IE00B4L5Y983EURXMIL", "FR0010655712EURXPAR"],
    start_date="2024-01-01",
    limit=1000
)

# Filter by criteria
filtered = client.securities.filter(filters={"Currency": "EUR", "TepiloraType": "ETF"})

# Get facets for building filters
facets = client.securities.facets(fields=["Currency", "TepiloraType", "Country"])

Portfolio

# Create portfolio
portfolio = client.portfolio.create(
    name="My Portfolio",
    input_type="fixed_weights",
    weights={"IE00B4L5Y983EURXMIL": 0.6, "FR0010655712EURXPAR": 0.4},
    start_date="2024-01-01"
)

# Get returns
returns = client.portfolio.returns(
    id=portfolio["portfolio"]["id"],
    start_date="2024-01-01",
    return_method="twr"
)

# Performance attribution
attribution = client.portfolio.attribution(
    id=portfolio["portfolio"]["id"],
    benchmark_weights={"IE00B4L5Y983EURXMIL": 0.5, "FR0010655712EURXPAR": 0.5},
    start_date="2024-01-01",
    end_date="2024-12-31"
)

# Optimize
optimized = client.portfolio.optimize(
    identifiers=["IE00B4L5Y983EURXMIL", "FR0010655712EURXPAR"],
    settings={
        "solver_mode": "risk_parity",
        "constraints": {"single_position_limit": 0.30}
    },
    start_date="2024-01-01"
)

Analytics

# List available functions
functions = client.analytics.list()

# Get function help
help_info = client.analytics.help("rolling_volatility")

# Calculate rolling volatility
vol = client.analytics.rolling_volatility(
    identifiers="IE00B4L5Y983EURXMIL",
    period=252,
    start_date="2023-01-01"
)

# Rolling Sharpe ratio
sharpe = client.analytics.rolling_sharpe(
    identifiers="IE00B4L5Y983EURXMIL",
    period=252,
    rf=0.02
)

# Factor regression
factors = client.analytics.factor_regression(
    identifiers="IE00B4L5Y983EURXMIL",
    model="FF5"
)

News & Publications

# Search news
news = client.news.search(query="bitcoin", limit=20)

# Latest news
latest = client.news.latest(limit=10)

# Trending topics
trending = client.news.trending(limit=50, finance_only=True)

# Search publications
pubs = client.publications.search(query="market outlook", limit=10)

Alerts

# List alerts
alerts = client.alerts.list(enabled=True)

# Evaluate an alert manually
result = client.alerts.evaluate(rule_id="your_rule_id")

Bonds

# Analyze bond (use full TepiloraCode, not plain ISIN)
analysis = client.bonds.analyze(identifier="DE000A2NBZ21EURXFRA")

# Screen bonds
bonds = client.bonds.screen(
    criteria={"min_yield": 4.0, "max_duration": 5.0},
    limit=50
)

# Get yield curve
curve = client.bonds.curve(currency="EUR", date="2024-01-15")

Arrow/Binary Formats

from Tepilora.arrow import read_ipc_stream

# Request Arrow format
resp = client.call_arrow_ipc_stream("securities.search", params={"query": "ETF", "limit": 1000})
table = read_ipc_stream(resp.content)
print(table.to_pandas())

Module-Level API

import Tepilora as T

# Configure globally
T.configure(api_key="YOUR_KEY")

# Use without client instance
T.analytics.rolling_volatility(identifiers="IE00B4L5Y983EURXMIL")

Or via environment variables:

export TEPILORA_API_KEY=your_key
export TEPILORA_BASE_URL=https://tepiloradata.com

Error Handling

from Tepilora.errors import TepiloraAPIError

try:
    data = client.securities.search(query="invalid")
except TepiloraAPIError as e:
    print(f"Error: {e.message}")
    print(f"Code: {e.status_code}")

API Endpoints

• POST /T-Api/v3 - Unified action router (all operations)
• GET /T-Api/v3/health - Health check
• GET /T-Api/v3/pricing - Pricing info
• GET /T-Api/v3/logs/status - Logs status

Version

import Tepilora
print(Tepilora.__version__)  # 0.5.0