u/BuildwithPublic

Historical options contract data is now live on the Public API. Full chain history. OHLCV bars on individual contracts. Historical quotes with bid/ask. Same API key you're already using for live execution.

Wanted to break down what this actually unlocks for anyone building systematic options strategies.

What shipped:

• Full chain history — pull what the options chain looked like on any past date, not just today's snapshot
• OHLCV bars on individual contracts — track how a specific strike traded over its life
• Historical bid/ask quotes — model realistic fills instead of midpoint fantasy math

What you can actually build now:

IV surface reconstruction. With historical chain data you can reconstruct the implied volatility surface at any point in time. Study how IV skew behaved into earnings, how the term structure shifted around FOMC, how put skew spiked during selloffs. Historical IV behavior is the foundation of any real options edge.

Realistic backtesting. Most retail options backtests use end-of-day midpoints as fill prices. With historical bid/ask you can model slippage properly. The difference between a backtest that uses mids and one that accounts for realistic fills can swing 30-40% of your apparent edge. It matters.

IV rank and percentile — computed from your own data. Both require historical IV to calculate correctly. Previously you had to source this from a separate provider or trust someone else's pre-computed number. Now you can derive it yourself from the same data source you're executing against.

IV crush quantification. If you're selling premium into earnings, you want to know how much IV collapses post-announcement on specific names, historically. Average IV going in vs. the morning after. Which names crush hardest. That analysis requires historical chain data — and now you can run it on your actual trading universe.

Put skew analysis. 25-delta put IV vs. ATM IV tracked historically. Is today's skew elevated relative to where it's been? Is the market paying up for tail risk or is it cheap? These filters separate systematic traders from people just selling premium into any environment.

Strategy backtesting with real contract prices. Wheel, 45 DTE strangle, 0DTE credit spread — you can now test entry and exit rules against historical data using actual options P&L, not just underlying price movement.

Why the same API key matters.

The execution/research split is the friction point that makes most algo setups messy. Polygon or CBOE for historical data, something else for live data, your broker's API for execution. Three systems, three auth flows, three schemas to normalize.

When your historical research data and your live execution data come from the same source with the same schema, your backtests are actually testing what your live system will see. That's a materially stronger foundation.

This is on top of what was already live — real-time chains, Greeks on the chain endpoint, preflight for multileg orders, cancel/replace, and the MCP server for agentic workflows. Historical data was the missing piece for anything signal-driven.

Docs at public.com/api/docs. Happy to answer questions in the comments!

We just updated our Python SDK, CLI, MCP server, and agent skills to include market data endpoints — specifically historical bars (1-min to 1-day, 5 years) and live quotes.

For anyone building on their API this is a meaningful update. Previously you had to pull market data from a separate source and route orders through Public. Now the data and execution layer are in the same SDK.

Practical use case: your Python strategy pulls historical bars to backtest, switches to live quotes at market open, and routes orders — all through one API key. Or if you're running MCP with Claude, the agent now sees live prices before constructing an order.

https://preview.redd.it/wbxk36rvl41h1.png?width=2444&format=png&auto=webp&s=c1f157f9d8b273eb3456eae77eee5e2e1400e84f

Docs at public.com/api/docs if you want to dig in.

DM me happy to get you moving on setups!

This one has been at the top of the request list for a while. Today it's live.

Historical bar data is now available on the Public API.

Here's what that means in practice:

Until today, the Public API was execution-first — live quotes, options chains, portfolio data, and order placement. Powerful for automation, but if you wanted to backtest a strategy or build a signal layer, you had to pull historical data from a separate vendor and stitch it together yourself. That extra layer added friction, cost, and complexity to every serious algo project built on Public.

That gap is now closed.

What's live today:

— OHLCV bar data for any supported symbol

— Periods from single-day intraday up to five years, plus YTD and since-purchase

— Aggregation control from 1-minute bars up to monthly

— Pre-market, regular session, and after-market data returned as separate segments

— Gain/loss vs. prior close included in every response

What this unlocks:

You can now backtest a strategy, validate signals against historical price action, and execute trades — all inside the same API, with the same credentials, with no third-party data dependency.

For anyone building AI trading agents: your agent now has memory. It can look back before it acts.

The execution side remains unchanged: zero commissions on options, a $0.10 per contract rebate, full multileg order support, and MCP integration for agent-native workflows.

Docs: public.com/api/docs

We'll be in the comments. What are you building?

Big unlock for anyone building AI trading agents on r/PublicApp

Historical bar data is now live. Here's what that actually means for your agent:

→ Backtest your strategy before going live — pull up to 5 years of OHLCV on any symbol

→ Feed historical bars into your signal layer — momentum, mean reversion, regime detection

→ Run intraday models with 1-minute resolution

→ Pre and after-market data included as separate segments

The execution side hasn't changed: $0.10/contract rebate, full multileg support. Docs + quickstart: public.com/api/docs

What are you building?

Been experimenting a lot with Agents at r/PublicApp and wanted to share what's possible for active options traders. I honestly never thought in my wildest dreams this tech would be available to retail traders in their brokerage account.

What I did was I described a simple 0DTE SPY workflow in plain English and the agent built for me and executed it autonomously against my live brokerage account.

Here's exactly what it does:

Every weekday at 9:45am ET it scans the SPY 0DTE call chain, filters for contracts with open interest above 1000 and a gamma/theta ratio above 3.0, buys 3 contracts of the top ranked setup, and exits automatically at 50% gain or 30% loss.

What's really interesting from a trader perspective is that you're not just automating execution -you're encoding your actual decision logic into the deterministic workflow. Greeks-based ranking, liquidity filters, risk gates. The agent runs the same process every morning without deviation-purely deterministic, no hallucinations.

Still super early but this feels like a genuine shift in how active traders will interact with the market. Happy to answer questions on the setup or help you try out agents- DM me

https://reddit.com/link/1t03g3x/video/92tg7ljp6dyg1/player