Polymarket Data Analysis for QuantConnect

$5-$10. Max for this expert information:

EXPERT REQUIRED: POLYMARKET HISTORICAL DATA ARCHITECTURE FOR QUANTCONNECT/LEAN

I need an expert to solve one specific problem in my existing Polymarket quantitative trading system.

With traditional futures trading, I can obtain large amounts of continuous historical data for the same underlying asset and use QuantConnect/LEAN to perform discovery, backtesting, walk-forward testing, out-of-sample validation, overfitting analysis, parameter optimization, and strategy validation.

Polymarket is fundamentally different because it consists of thousands of individual prediction markets that open, evolve, and resolve at different times. My concern is that my current LEAN bridge does not have enough historical Polymarket data in a format that allows it to perform the same type of quantitative discovery and validation that I would normally perform with years of futures data.

I do NOT simply want to start the bot today and wait months or years for it to generate its own dataset.

I need an EXPERT to determine exactly what historical data can realistically be obtained from the Polymarket API and related publicly available Polymarket data sources, and how that data should be transformed into a format that my QuantConnect/LEAN engine can actually use for research.

Specifically, I want you to investigate and design a historical-data solution covering, where available:

- Complete historical trades
- Trade price
- Trade size
- Timestamp
- Buy/sell direction
- Market/token ID
- Market creation and closing dates
- Market resolution/outcome
- Historical price series
- Historical volume
- Historical activity/trade frequency
- Wallet/trader activity
- Position changes where reconstructable
- Historical liquidity information
- Historical order-book information if available from any legitimate source
- Market metadata and categories
- Related/linked markets
- Any other historical variables that could be useful for quantitative discovery

The critical question is:

HOW DO WE TURN MANY DIFFERENT HISTORICAL POLYMARKET MARKETS INTO A LARGE, STRUCTURED, REUSABLE RESEARCH DATASET THAT LEAN CAN USE TO DISCOVER GENERALIZABLE TRADING PATTERNS?

I do NOT want to simply concatenate unrelated markets and pretend they are one continuous asset.

I want the system to recognize that each market has its own lifecycle while extracting normalized features that can be compared across thousands or millions of historical market events.

For example, I want to be able to research patterns such as:

MARKET OPENS
→ ACTIVITY INCREASES
→ LIQUIDITY CHANGES
→ TRADE IMBALANCE APPEARS
→ PRICE IMPULSE
→ CONTINUATION/EXHAUSTION
→ EXIT
→ FINAL OUTCOME

Then determine whether similar sequences across many different Polymarket markets historically produced statistically significant outcomes.

I need the expert to determine:

1. What historical Polymarket data actually exists and can legally/reliably be collected.

2. What data is available directly through the API versus other legitimate public data sources.

3. How far back each dataset can realistically go.

4. Which data can be reconstructed historically and which cannot.

5. Whether historical order-book depth can be reconstructed or whether it is fundamentally unavailable.

6. How to normalize different markets so they can be used as comparable observations without creating survivorship bias or look-ahead bias.

7. How to construct a LEAN-compatible historical data format from this information.

8. How to create a large historical event dataset that LEAN can replay as though the events were occurring in real time.

9. How to preserve market lifecycle information so the system understands when a market opened, developed, resolved, and closed.

10. How to perform proper discovery, backtesting, walk-forward testing, out-of-sample testing, and overfitting analysis using this multi-market dataset.

11. How to prevent future information from accidentally leaking into historical features.

12. How to determine whether a discovered pattern generalizes across DIFFERENT markets rather than merely working on one particular market.

13. Whether historical wallet behavior can be incorporated as an additional research feature.

14. Whether thousands of short-lived markets can collectively provide the statistical sample size that I am currently missing.

15. What data should be stored going forward so the live bot continuously expands the historical research dataset.

The final objective is to create a historical Polymarket research environment where LEAN can discover and validate strategies BEFORE I have to wait years for the live system to accumulate enough observations.

I am specifically looking for someone with strong experience in quantitative research, historical market-data engineering, Polymarket/API data, LEAN/QuantConnect, statistical validation, and avoiding backtest overfitting.

Please do not simply tell me "Polymarket doesn't have futures-style continuous historical data."

I already understand that.

I need you to solve the engineering/research problem of extracting the maximum amount of legitimate historical information that DOES exist and transforming the different Polymarket markets into a statistically valid research dataset that my LEAN engine can actually use.

Please provide a concrete proposed data architecture, available historical data sources, estimated historical depth, normalization methodology, LEAN data format, and recommended research/backtesting methodology.