TickerDB - Pre-computed market data for agents.

Connect your agent to hundreds of indicators like trend_direction, support_level, and analyst_consensus to improve reasoning and reduce token usage.

• Sync and async clients
• Full type hints for IDE autocompletion
• Typed exceptions for every error class
• Rate limit information on every response
• Covers the full v1 API: summaries, search, OHLCV, and watchlists

Full API documentation: https://tickerdb.com/docs

Installation

pip install tickerdb

Quick Start

Synchronous

from tickerdb import TickerDB

client = TickerDB("tdb_your_api_key")

# Get a ticker summary
result = client.summary("AAPL")
print(result["data"])
print(result["data"]["as_of_date"])

# Rate limit info is included on every response
print(result["rate_limits"]["requests_remaining"])

Asynchronous

import asyncio
from tickerdb import AsyncTickerDB

async def main():
    async with AsyncTickerDB("tdb_your_api_key") as client:
        result = await client.summary("AAPL")
        print(result["data"])

asyncio.run(main())

Endpoints

Summary

Get a detailed summary for a single ticker.

result = client.summary("AAPL")
result = client.summary("AAPL", timeframe="weekly")
result = client.summary("AAPL", date="2025-01-15")

Summary payloads are intentionally forward-compatible. Current snapshots include top-level freshness like as_of_date, same-candle ohlcv.open/high/low/close/volume, richer volume fields such as price_direction_on_volume, raw support/resistance prices such as support_level.level_price, optional level metadata such as support_level.status_meta when requested, Pro sector_context fields like agreement and overbought_count, and stock-only fundamentals such as fundamentals.free_cash_flow and nested fundamentals.insider_activity when available.

Summary stays band-first by default, so sibling _meta / status_meta stability objects are omitted unless you opt in:

result = client.summary("AAPL", meta=True)
result = client.summary(
    "AAPL",
    fields=["trend.direction", "trend.direction_meta", "fundamentals.free_cash_flow"],
)

MA distance fields are available both in snapshots and events:

result = client.summary("AAPL", fields=["trend.distance_from_ma_band.ma_50"])
print(result["data"]["trend"]["distance_from_ma_band"]["ma_50"])
# "proximity_above"

Semantic MA fields are available in the same trend object:

result = client.summary(
    "AAPL",
    fields=[
        "trend.ma_slopes.ma_8",
        "trend.ma_slopes.ma_20",
        "trend.ma_slopes.ma_40",
        "trend.ma_slopes.ma_50",
        "trend.ma_slopes.ma_100",
        "trend.ma_slopes.ma_200",
        "trend.ma_compression_band",
        "trend.ma_crossover_event",
    ],
)

Summary with Date Range

Get a summary series for one ticker across a date range by passing start and end.

result = client.summary("AAPL", start="2025-01-01", end="2025-03-31")
result = client.summary("AAPL", timeframe="weekly", start="2024-01-01", end="2025-03-31")

Summary with Events Filter

Query event occurrences for a specific band field.

result = client.summary("AAPL", field="momentum_rsi_zone", band="deep_oversold")
result = client.summary("AAPL", field="extremes_condition", band="deep_oversold")
result = client.summary("AAPL", field="fundamentals_free_cash_flow", band="moderate_surplus")
result = client.summary("BTCUSD", field="trend_distance_ma50", band="above")
result = client.summary(
    "BTCUSD",
    field="trend_distance_ma50",
    band="above",
    context_ticker="SPY",
    context_field="trend_distance_ma50",
    context_band="below",
)

For MA distance event fields such as trend_distance_ma50, grouped band="above" and band="below" aliases are supported in addition to granular values like proximity_above.

Use stats=True when you want aggregated outcomes instead of raw event rows:

result = client.summary(
    "SOLUSD",
    field="trend_distance_ma20",
    band="above",
    context_ticker="QQQ",
    context_field="trend_distance_ma20",
    context_band="above",
    before="2025-07-01",
    stats=True,
)
print(result["data"]["stats"])

Watchlist

Get the saved watchlist snapshot for the authenticated account.

result = client.watchlist()
print(result["data"]["as_of_date"])
result = client.watchlist(date="2025-01-15")

Add tickers to the saved watchlist:

result = client.add_to_watchlist(["AAPL", "MSFT", "TSLA"])

Remove tickers from the saved watchlist:

result = client.remove_from_watchlist(["TSLA"])

Watchlist Changes

Get field-level state changes for your saved watchlist tickers since the last pipeline run.

result = client.watchlist_changes()
result = client.watchlist_changes(timeframe="weekly")

Band Stability Metadata

Summary omits sibling _meta objects by default so the primary band label stays front-and-center. Set meta=True to include full paid-tier stability metadata across the response, or request just the few *_meta fields you need via fields.

Summary and watchlist responses also include as_of_date so you can tell which market session the snapshot represents.

result = client.summary("AAPL", meta=True)
data = result["data"]

# The band value itself
print(data["trend"]["direction"])          # "uptrend"

# Stability metadata for that band
print(data["trend"]["direction_meta"])
# {"stability": "established", "periods_in_current_state": 18, "flips_recent": 1, "flips_lookback": 20}

# Type hints available
from tickerdb import Stability, BandMeta

Stability is one of "fresh", "holding", "established", or "volatile". BandMeta contains the full metadata dict. Stability metadata is available on Plus and Pro tiers only.

Stability context also appears in Watchlist, which still includes paid-tier _meta objects by default, and in Watchlist Changes, which include stability fields inline for each changed band.

Query Builder

The SDK includes a fluent query builder for searching assets by categorical state. Chain methods in order: select, filters, sort, limit.

results = client.query() \
    .select('ticker', 'sector', 'trend_distance_ma50', 'momentum_rsi_zone', 'fundamentals_free_cash_flow') \
    .eq('trend_distance_ma50', 'proximity_above') \
    .eq('fundamentals_free_cash_flow', 'moderate_surplus') \
    .eq('sector', 'Technology') \
    .sort('extremes_condition_percentile', 'asc') \
    .limit(10) \
    .execute()

Pass .date("2025-01-15") (or client.search(..., date="2025-01-15")) to run the query against a point-in-time snapshot. History depth is capped by your plan.

Account

Check your plan tier, limits, usage, and credit balance. This call does not consume your monthly request quota.

result = client.account()
print(result["data"]["usage"]["monthly_requests_remaining"])
print(result["data"]["usage"]["credit_balance"])

OHLCV

Get daily OHLCV bars for a ticker (split/dividend-adjusted for equities, unadjusted for crypto). Results are cursor-paginated and credit-metered (100 bars per credit, minimum 1). History depth is capped by your plan.

result = client.ohlcv("AAPL", start="2025-01-01", end="2025-03-31")
for bar in result["data"]["bars"]:
    print(bar["date"], bar["close"])

# Follow the cursor manually...
if result["data"]["has_more"]:
    nxt = client.ohlcv("AAPL", cursor=result["data"]["next_cursor"])

# ...or stream every bar across pages automatically
for bar in client.iter_ohlcv("AAPL", start="2025-01-01"):
    print(bar["date"], bar["close"])

If a request would exceed your credit balance, InsufficientCreditsError is raised with credits_required and credits_remaining attributes.

Error Handling

The SDK raises typed exceptions for all API errors:

from tickerdb import TickerDB, TickerDBError, RateLimitError, NotFoundError

client = TickerDB("tdb_your_api_key")

try:
    result = client.summary("INVALID_TICKER")
except NotFoundError as e:
    print(f"Ticker not found: {e.message}")
except RateLimitError as e:
    print(f"Rate limited! Resets at: {e.reset}")
    print(f"Upgrade: {e.upgrade_url}")
except TickerDBError as e:
    print(f"API error [{e.status_code}]: {e.message}")

Exception Hierarchy

InsufficientCreditsError subclasses RateLimitError (so existing except RateLimitError handlers still catch it) and adds credits_required and credits_remaining attributes.

All exceptions include status_code, error_type, message, and optionally upgrade_url and reset attributes.

Rate Limits

Every response includes a rate_limits dict parsed from the API headers:

result = client.summary("AAPL")
limits = result["rate_limits"]

print(limits["request_limit"])           # Total request limit
print(limits["requests_remaining"])      # Requests remaining
print(limits["request_reset"])           # Reset timestamp
print(limits["hourly_request_limit"])    # Hourly limit
print(limits["hourly_requests_remaining"])  # Hourly remaining

Development

Requires Python 3.8+.

git clone https://github.com/tickerdb/tickerdb-python
cd tickerdb-python

python -m venv .venv
source .venv/bin/activate          # Windows (PowerShell): .venv\Scripts\Activate.ps1

pip install -e ".[dev]"            # editable install + dev tools

Run the checks (no API key required — the suite uses a mocked transport):

pytest
ruff check src tests

examples/smoke_test.py runs a read-only check against the live API using your key:

TICKERDB_API_KEY=tdb_your_key python examples/smoke_test.py

Links

• TickerDB Website
• API Documentation
• PyPI Package