Specialist Needed: Transform Existing Quant Bridge Into a Native Polymarket Data-Analysis & Decision-Execution Engine

$10-$40 Max for completed project

This Bridge is already smart: just needs formatting and adaptation to Polymarket:

Specialist Needed: Transform Existing Quant Bridge Into a Native Polymarket Data-Analysis & Decision-Execution Engine
I have an existing quantitative trading framework called Quant Bridge that was originally designed for trading analysis.
I want to explore a fundamentally different application of it:
I want to transform Quant Bridge into a dedicated Polymarket Data-Analysis & Decision-Execution Engine.
IMPORTANT — THIS IS NOT A COPY-TRADING BOT PROJECT
I already have a separate Polymarket copy-trading bot that follows a highly ranked wallet and performs additional consensus analysis.
I do NOT want you to take that bot and simply add Quant Bridge functionality to it.
I also do not want to assume that any of the existing copy-trading logic, rules, wallet rankings, entry rules, exit rules, or assumptions belong in the new system.
Instead, I want the Quant Bridge treated as the core analytical engine, and I want the Polymarket API/data environment connected to it cleanly from the ground up.
The question I want answered is:
If we give a powerful quantitative analysis engine clean, properly structured Polymarket data with as few assumptions as possible, what is the best architecture for allowing that engine to discover useful relationships, generate trading decisions, and ultimately execute those decisions?
I want the specialist to help determine that—not have me prescribe the answer beforehand.
THE CONCEPT
The architecture should essentially become:
POLYMARKET DATA/API
↓
DATA NORMALIZATION / FEATURE LAYER
↓
QUANT BRIDGE
↓
CONTINUOUS MARKET ANALYSIS
↓
SIGNAL / DECISION ENGINE
↓
EXECUTION ENGINE
↓
PORTFOLIO / OUTCOME DATA
↓
FEEDBACK INTO QUANT BRIDGE
The Quant Bridge should be the brain.
Polymarket should provide the raw environment/data.
The execution layer should be the hands.
I don't want to predetermine exactly what relationships the engine should find.
WHAT I NEED FROM THE SPECIALIST
First, I want you to study the existing Quant Bridge architecture and determine what it is actually capable of doing.
Then determine the cleanest way to connect Polymarket's available APIs/data to it.
I want the Polymarket data converted into a structured format that is mathematically and statistically useful to the Quant Bridge.
The objective is to avoid unnecessarily forcing traditional futures-market assumptions onto Polymarket.
Polymarket is a different market structure, so I want the system designed around Polymarket's actual data and mechanics.
DATA ARCHITECTURE
I want you to determine what information the Quant Bridge should receive.
Potential data sources may include, where available:
Market prices
YES/NO prices
Order-book data
Bid/ask
Depth
Liquidity
Volume
Trades
Trade size
Trade direction
Timestamp
Market creation time
Market expiration
Resolution status
Wallet activity
Wallet positions
Position changes
Wallet transaction history
Market participation
Price movement
Volume changes
Liquidity changes
Market-to-market relationships
Wallet-to-wallet relationships
Timing relationships
Other Polymarket-specific information the specialist determines is useful
I do not want all of this blindly dumped into Quant Bridge.
I want the specialist to determine:
What is signal, what is noise, what should be normalized, what should become a feature, and what should remain raw data?
NO PREDEFINED TRADING ASSUMPTIONS
This is extremely important.
I don't want the developer to begin with:
"Let's copy the top wallet."
I want the system capable of discovering whether that is actually the best approach.
For example, perhaps the strongest signal comes from:
A particular combination of wallets
Trade timing
Liquidity changes
Order-book imbalance
Price/volume relationships
Multiple wallets independently entering
One wallet consistently leading others
Market probability dislocations
Historical behavior around similar markets
Changes in wallet positioning
Relationships between different markets
Or something I haven't considered
I want the Quant Bridge to discover these relationships rather than having me hard-code them beforehand.
CONTINUOUS ANALYSIS
The finished system should not simply wait for a wallet to make a trade.
It should continuously analyze the Polymarket environment.
The engine should be capable of continuously evaluating:
What is happening?
What has changed?
What relationships are developing?
Which variables are historically associated with successful outcomes?
Is there currently an actionable opportunity?
How strong is the signal?
What is the appropriate action?
That action could ultimately be:
BUY / SELL / HOLD / REDUCE / EXIT / NO TRADE
depending on what the quantitative analysis supports.
DECISION ENGINE
I want Quant Bridge to ultimately produce a structured decision rather than simply outputting raw analytics.
For example:
MARKET: XXXXX

SIGNAL: 87/100

DIRECTION: YES

EXPECTED EDGE: X%

CONFIDENCE: HIGH

RECOMMENDED EXPOSURE: X%

ENTRY CONDITIONS: XXXXX

EXIT CONDITIONS: XXXXX

RISK CONDITIONS: XXXXX

REASONS:
- Signal A
- Signal B
- Signal C

DATA QUALITY: HIGH
The exact structure is up to the architect.
The point is that analysis must ultimately become an executable decision.
EXECUTION
Once the analytical architecture has been validated, I want the system capable of connecting the decision engine to Polymarket execution.
However, execution should be separated from analysis.
The Quant Bridge should be able to say:
"This is the trade I believe has the strongest measurable opportunity."
The execution layer then determines how to implement that decision based on:
Available balance
Market liquidity
Current price
Spread
Slippage
Order size
Execution conditions
Position limits
This separation is important because I don't want execution mechanics contaminating the analytical engine.
FEEDBACK LOOP
The system should record what happened after every decision.
For each signal, record:
What the engine saw
What features were present
What decision it made
Confidence
Expected edge
Entry
Actual execution
Exit
Result
P&L
Market resolution
Slippage
Whether the prediction was correct
Whether the signal should have been acted upon
This creates a continuously growing dataset that can be used to determine which signals actually have predictive value.
TESTING
Before allowing the engine to trade meaningful capital, I want:
Phase 1
Connect Polymarket data to Quant Bridge.
Phase 2
Validate the data normalization and feature pipeline.
Phase 3
Run the Quant Bridge in analysis-only mode.
Phase 4
Generate hypothetical decisions without executing them.
Phase 5
Backtest / replay historical data where technically possible.
Phase 6
Compare predicted outcomes against actual market outcomes.
Phase 7
Run the system in paper/shadow trading.
Phase 8
Only after the analytical system demonstrates measurable edge should live execution be considered.
THE MOST IMPORTANT PART OF THIS PROJECT
I am not hiring someone to make my existing copy bot more complicated.
I am hiring someone to determine whether my existing Quant Bridge can become a much more sophisticated Polymarket-native quantitative intelligence engine.
I want you to approach this as an architecture and quantitative research problem first and a programming problem second.
Don't assume that my existing copy-trading strategy is correct.
Don't assume that top-wallet consensus is the best signal.
Don't assume that traditional futures indicators belong here.
Don't assume that every available API field should be used.
Start with the Polymarket data itself and determine what information can actually create a measurable trading edge.
Then design the cleanest possible bridge between that information and Quant Bridge.
IDEAL SPECIALIST
I am specifically looking for someone with experience in several of the following:
Quantitative trading systems
Blockchain architecture
Polymarket
EVM/Polygon
Market microstructure
Statistical modeling
Real-time data systems
Algorithmic execution
Quant research
Data normalization
Backtesting
Machine learning / feature engineering where appropriate
Trading-system architecture
A general Python developer who can connect an API is not what I am looking for.
I need someone capable of looking at the existing Quant Bridge and saying:
"Here is what this engine actually does well. Here is how Polymarket's data differs from traditional financial-market data. Here is the information we should feed it. Here is what we should deliberately NOT feed it. And here is the architecture that gives it the best opportunity to discover a genuine edge."
I understand that no system can guarantee profits or a continuously rising equity curve. I am looking for the best possible engineering and quantitative research process to discover and exploit a measurable edge—not fabricated performance or guaranteed returns.
The ultimate goal is a clean, Polymarket-native Quant Bridge that continuously analyzes the market, generates data-driven decisions, and can eventually execute those decisions autonomously.