DEV Community: Benjamin-Cup

Your Trading Backtest Might Be Cheating: Understanding Look-Ahead Bias

Benjamin-Cup — Fri, 25 Sep 2026 14:00:30 +0000

Have you ever built a trading strategy that achieved an 80–90% win rate in backtesting, only to lose money after going live?

Before blaming latency, slippage, or the market, check one thing:

Is your backtest using information from the future?

This is called look-ahead bias.

I've encountered this while developing Polymarket trading bots. A strategy can look extremely profitable in historical testing simply because the backtest has access to information that a live bot wouldn't have.

Let's look at some common examples.

What Is Look-Ahead Bias?

The basic rule is simple:

A trading decision can only use information that was available at that exact moment.

Imagine your bot enters at the beginning of a candle.

This is a problem:

signal = df["close"][i] > df["open"][i]
enter_at = df["open"][i]

The candle hasn't finished yet, so the closing price isn't available.

Instead:

signal = df["close"][i - 1] > df["open"][i - 1]
enter_at = df["open"][i]

Now the signal is based on a completed candle.

That small difference can have a huge impact on backtest results.

1. Future Candle Data

This is one of the most common mistakes.

If your strategy makes a decision at time t, it shouldn't know:

future close
future high/low
future volume
future returns
future indicators

A historical dataset contains all of this information.

A live trading bot doesn't.

Your backtest needs to enforce the same timeline.

2. Future Daily Statistics

Consider a strategy that trades at 9:00 AM.

If its features include:

Today's High
Today's Low
Today's Volume
Today's Close

there's a problem.

At 9:00 AM, most of these values aren't final.

Because your historical dataset already contains the complete day, your code might accidentally expose the future to the strategy.

Every feature should have a clear question:

When did this information become available?

If the answer is after the trade decision, it shouldn't be used.

3. Data Preprocessing Can Leak Information

Look-ahead bias can also happen before your strategy even runs.

For example:

# WRONG
scaler.fit(all_data)
X = scaler.transform(all_data)

The scaler has now seen the entire dataset, including future observations.

A more realistic approach is:

# CORRECT
scaler.fit(data[:t])
X_now = scaler.transform(data[t])

For walk-forward testing, preprocessing needs to follow the same timeline as the strategy.

4. Centered Rolling Indicators

Be careful with rolling calculations too.

For example:

# WRONG
df["ma"] = df["close"].rolling(
    20,
    center=True
).mean()

A centered window can use prices from both before and after the current timestamp.

Use a trailing window instead:

# CORRECT
df["ma"] = df["close"].rolling(20).mean()

The indicator should only depend on information that has already happened.

5. Leaked Labels

This is especially dangerous for machine-learning strategies.

Suppose you're trying to predict the outcome of a prediction market.

If your input features accidentally contain:

settlement results
resolution prices
future returns
post-resolution data
future market statistics

then the model isn't really predicting anything.

It's seeing the answer.

You can easily end up with a model showing 99% accuracy in testing that performs terribly in production.

Why Does Look-Ahead Bias Produce Great Results?

Because future information is extremely valuable.

If your strategy knows what happens next, it becomes much easier to make profitable decisions.

That's why look-ahead bias can create:

extremely high win rates
smooth equity curves
unrealistic Sharpe ratios
tiny drawdowns
suspiciously consistent returns

The dangerous part is that nothing crashes.

The code works.

The backtest completes.

The results look great.

The problem is the timeline.

A Simple Way to Structure a Backtest

One approach I use is to make the historical boundary explicit:

for t in range(start, end):

    history = data[:t]

    decision = strategy(history)

    outcome = data[t]

    record(decision, outcome)

The strategy receives:

Past → Decision

The backtest then evaluates:

Decision → Future Outcome

The strategy should never get access to the second part when making the decision.

Historical Data Matters

This became particularly important for my Polymarket research.

I've been recording historical market data directly from on-chain sources and the Polymarket API instead of relying only on reconstructed datasets.

I archive 5-minute cryptocurrency market data locally and use it for strategy development and backtesting.

Having your own historical dataset gives you much more control over:

timestamps
market state
price history
market lifecycle
missing data
available information
backtesting assumptions

I've used this infrastructure while researching and developing several Polymarket trading strategies, including end-cycle and BTC/ETH hedge strategies.

The goal isn't to produce an impressive backtest.

The goal is to produce a backtest that behaves like the real trading environment.

Backtesting Still Isn't Live Trading

Even after removing look-ahead bias, a backtest is still a simulation.

You may need to account for:

latency
slippage
liquidity
order-book depth
partial fills
rejected orders
API delays
trading fees
position limits
changing market conditions

A realistic backtest should answer:

Could my bot actually have made this decision with the information available at that moment?

That's much more useful than simply asking:

Was the strategy profitable on historical data?

Final Takeaway

When a backtest looks too good to be true, check the timeline before changing the strategy.

For every feature, indicator, and dataset, ask:

When did this information become available?

If it became available after the trading decision, it's future information.

And if your backtest can see the future, your strategy isn't being tested.

It's being given the answer.

More Polymarket Bot Development

I'm building and researching Polymarket trading infrastructure, historical data collection, automated execution, and backtesting.

You can find some of my work here:

GitHub:

https://github.com/Benjam1nCup/Polymarket-trading-bot-python-V2

Telegram:

https://t.me/BenjaminCup

If you're working on trading bots, prediction markets, or quantitative research, I'd be interested in hearing about your approach.

Building a Two-Sided Limit Order Bot for Polymarket BTC 5-Minute Markets

Benjamin-Cup — Thu, 24 Sep 2026 17:36:55 +0000

After the TWAP upgrade, I started testing a different approach for Polymarket's BTC 5-minute Up/Down markets.

The main thing I noticed was that short-term prices became much more active.

Instead of continuously taking the current price, I'm experimenting with placing limit orders on both outcomes and trying to capture temporary price movement.

This post explains the idea and the engineering behind it.

This is an experimental strategy, not a guaranteed-profit system.

The Basic Idea

A BTC 5-minute market has two outcomes:

UP
DOWN

For example:

UP   best ask = 0.60
DOWN best ask = 0.40

Instead of buying at the current ask, the bot places orders below it.

For example, with an 0.08 offset:

UP order   = 0.60 - 0.08 = 0.52
DOWN order = 0.40 - 0.08 = 0.32

So the bot places:

BUY UP   @ 0.52
BUY DOWN @ 0.32

The goal is to have both orders filled during short-term market movement.

Why Both Sides?

The interesting part is the combined position.

Suppose both orders fill:

UP   = 0.52
DOWN = 0.32

Total = 0.84

If both outcomes are held through settlement, the combined position has a $1 settlement value.

So the basic calculation is:

combined_cost = up_price + down_price

potential_edge = 1.00 - combined_cost

In this example:

potential_edge = 1.00 - 0.84
               = 0.16

This is only a gross theoretical edge. Real execution needs to account for fees, partial fills, timing, and other costs.

The Real Problem: Partial Fills

This is where the strategy gets interesting.

Suppose:

UP   → FILLED @ 0.52
DOWN → NOT FILLED

Now the bot isn't holding a paired position.

It has directional exposure to UP.

Therefore, the bot needs to track inventory separately:

UP inventory
DOWN inventory
Paired inventory
Unpaired inventory

For example:

UP   = 100
DOWN = 70

Paired = 70
Unpaired UP = 30

This distinction should be part of the position manager.

Dynamic Order Distance

I don't want to hardcode:

best_ask - 0.08

forever.

Market activity changes, so the order distance should be configurable and potentially dynamic.

A simple first version could use:

Low movement     → smaller offset
Medium movement  → medium offset
High movement    → larger offset

The bot can measure short-term movement:

price_change_1s
price_change_3s
price_change_5s

and use that information when calculating the next order price.

The exact values should come from historical/live testing.

Chainlink / TWAP Data

The order book isn't the only input.

The bot also needs the reference price information:

Chainlink price
TWAP
UP price
DOWN price
Time remaining

A simplified flow is:

Chainlink / TWAP
       ↓
Market state
       ↓
Order book
       ↓
Strategy engine
       ↓
Limit orders

This helps the bot understand the market state instead of reacting only to the latest order-book update.

Order Lifecycle

The execution loop can be kept relatively simple.

1. Read market data

UP bid/ask
DOWN bid/ask
Chainlink/TWAP
time remaining
inventory

2. Calculate target prices

UP target
DOWN target
combined cost
potential edge

3. Submit orders

BUY UP
BUY DOWN

4. Monitor

Watch:

order status
fills
price movement
inventory

5. Reprice

If the market moves enough:

cancel old orders
calculate new prices
submit new orders

6. Manage inventory

If only one side fills, the risk manager decides what to do with the unpaired position.

Avoiding Stale Orders

Fast markets make stale orders dangerous.

For example:

UP ask = 0.60

Our bid = 0.52

A few seconds later:

UP ask = 0.72

The original order may no longer make sense relative to the current market state.

The bot therefore needs an order-refresh mechanism.

Conceptually:

if market_move > repricing_threshold:
    cancel_order()
    calculate_new_price()
    submit_order()

The threshold should be tested rather than chosen arbitrarily.

Time Remaining

The same strategy behaves differently depending on how much time is left in the market.

For example:

4 minutes remaining

gives the bot considerably more time to complete a pair than:

10 seconds remaining

So time_remaining should be a first-class strategy variable.

A simple approach:

More time
    → normal quoting

Less time
    → reduce new exposure

Very close to settlement
    → strict inventory controls

Architecture

For the implementation, I would separate the system into several components:

┌─────────────────────┐
│    Market Data      │
│                     │
│ Order Book          │
│ Chainlink / TWAP    │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│   Strategy Engine   │
│                     │
│ Price calculation   │
│ Volatility          │
│ Pair calculation    │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│    Order Manager    │
│                     │
│ Place               │
│ Cancel              │
│ Reprice             │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Position Manager   │
│                     │
│ UP inventory        │
│ DOWN inventory      │
│ Paired inventory    │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│    Risk Manager     │
└─────────────────────┘

Keeping these components separate makes it much easier to test the strategy without connecting the execution layer immediately.

Start With a Simulator

Before running this with real capital, I would first replay live order-book data.

For every market update, store:

timestamp
Chainlink price
TWAP
time remaining

UP bid
UP ask

DOWN bid
DOWN ask

simulated UP order
simulated DOWN order

UP fill
DOWN fill

Then test different offsets:

0.05
0.06
0.07
0.08
0.09
0.10

The important metrics are:

Two-sided fill rate
One-sided fill rate
Average paired cost
Average time to pair
Maximum unpaired inventory

This is more useful than looking only at total P&L.

The Core Calculation

The first version of the strategy can be reduced to:

up_target = up_best_ask - offset
down_target = down_best_ask - offset

combined_cost = up_target + down_target

potential_edge = 1.0 - combined_cost

But the execution engine should not automatically trade just because potential_edge is positive.

It also needs to consider:

fill probability
time remaining
inventory
market movement
fees
order-book liquidity

The goal is not simply to find a cheap theoretical pair.

The goal is to determine whether the pair can actually be executed.

What I Want to Measure

The main questions I'm testing are:

How often do both orders fill?
How often does only one side fill?
How long does it take to complete a pair?
Which offset gives the best fill behavior?
How does TWAP movement affect fills?
How does the strategy behave near settlement?
How much unpaired inventory accumulates?

These measurements should tell us whether the strategy is actually viable as an execution system.

Final Thoughts

The interesting part of this strategy isn't simply:

"Buy both sides."

The difficult engineering problem is managing everything that happens between the two fills.

Market data
     ↓
Calculate quote
     ↓
Place both orders
     ↓
One order fills
     ↓
Track exposure
     ↓
Wait / reprice / manage
     ↓
Second order fills
     ↓
Paired position

That's the part I'm currently experimenting with after the TWAP upgrade.

I'll be testing the strategy using live market data first, then refining the execution and risk-management logic based on actual fill behavior.

Resources

I'm documenting my Polymarket bot research and experiments here:

GitHub:
https://github.com/Benjam1nCup/Polymarket-trading-bot-python-V2

The repository contains educational material, strategy research, and implementation experiments around automated Polymarket trading.

Contact:
https://telegram.me/BenjaminCup

If you're interested in discussing Polymarket bot development, trading infrastructure, or strategy research, feel free to reach out.

How I Built an Inventory-Balanced Trading Strategy for Polymarket

Benjamin-Cup — Wed, 23 Sep 2026 13:32:06 +0000

Most trading bots focus on one question:

When should I buy or sell?

While building my latest Polymarket strategy, I found another question to be just as important:

What happens after the trade?

A signal is easy to create. Managing inventory, failed hedges, stale orders, and changing market conditions is much harder.

So I built the strategy around three main ideas:

Detect meaningful price movement
Execute trades in smaller cycles
Keep inventory and risk under control

The Basic Strategy

Polymarket markets have complementary outcomes such as:

YES
NO

Instead of trying to predict which outcome will eventually win, this strategy looks for temporary pricing inefficiencies and manages both sides.

For example:

YES = $0.58
NO  = $0.49

The strategy can attempt to exploit the relationship between the two sides while controlling execution and inventory risk.

I consider this closer to inventory-balanced trading than pure arbitrage.

1. Laddered Execution

Instead of executing a large position at once, the bot divides it into smaller cycles.

Example:

100 YES
100 NO

5 cycles
20 shares per cycle

A cycle looks like:

Sell YES 20
     ↓
Wait for opposite side
     ↓
Sell NO 20
     ↓
Cycle complete

This helps reduce execution impact and makes inventory easier to manage.

2. Momentum + Reversal Detection

The first version of the strategy reacted to simple price spikes:

if price_rise >= 0.03:
    execute_sell()

That was too sensitive to short-term noise.

The updated strategy waits for:

Uptrend
   ↓
Local peak
   ↓
Reversal

A simplified uptrend check:

def uptrend_detected(prices):
    return (
        prices[-6] <
        prices[-5] <
        prices[-4] <
        prices[-3] <
        prices[-2]
    )

Then the bot checks whether a local peak has formed:

def peak_detected(prices):
    return (
        prices[-2] > prices[-3]
        and prices[-2] > prices[-1]
    )

The trade is only considered when the movement is also strong enough.

3. Inventory Is the Important Part

Imagine the bot executes:

SELL YES

but the opposite side never reaches the expected price.

Now the bot has an unbalanced position.

That is why the system tracks:

Current inventory
Completed cycles
Pending hedges
Time since first execution
Remaining market time

The strategy should always know exactly what position it has.

4. Hedge Protection

The bot doesn't wait forever for the perfect hedge.

For example, one protection rule can trigger when the opposite token stays below:

$0.20

for:

15 seconds

Another rule becomes active when the market approaches settlement.

With around:

20 seconds remaining

the bot becomes more aggressive about closing the remaining position.

There is also a maximum hedge timeout:

60 seconds

If the normal hedge hasn't happened by then, the system exits using a more aggressive limit price.

The principle is simple:

Every trading cycle needs an escape path.

5. Limit Orders + Stale Order Protection

The strategy uses limit orders to maintain control over execution price.

But limit orders can become stale.

So an unfilled order is cancelled after:

15 seconds

The process becomes:

Submit
  ↓
Wait
  ↓
Filled?
 ├── Yes → Complete
 └── No  → Cancel → Re-evaluate

This prevents old orders from remaining active after market conditions have changed.

6. Trading Window

The bot also limits when new cycles can start.

Current configuration:

trade_start_seconds: 300
trade_stop_seconds: 90

trend_strength_threshold: 0.03
first_leg_min_price: 0.50

force_hedge_opposite_price_threshold: 0.20
force_hedge_delay_seconds: 15

order_cancel_seconds: 15
hedge_timeout_seconds: 60

These values are strategy parameters, not universal numbers. Different markets can behave very differently.

The Main Lesson

The biggest lesson from building automated prediction-market systems is:

The trading signal is only one part of the strategy.

A complete system needs:

Market Data
     ↓
Signal
     ↓
Execution
     ↓
Inventory
     ↓
Hedge
     ↓
Risk Control

A good entry signal does not help much if the bot cannot manage what happens afterward.

That's why I increasingly think about trading bots as decision-making systems under uncertainty, rather than simply automated buy/sell scripts.

Resources

Polymarket Docs:
https://docs.polymarket.com/

GitHub:

Benjam1nCup / Polymarket-trading-bot-python-V2

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

An open-source and Strong Strategy collection of Polymarket trading bot and Polymarket arbitrage bot and Polymarket TWAP trading bot in Python for high-performance automated trading on polymarket crypto 5min and 15min markets.

This repository is primarily intended for educational and research purposes. It includes strategy concepts, implementation approaches, and selected performance screenshots to help developers understand how different automated trading strategies can be designed and tested.

The repository does not provide a complete production-ready trading bot source code. Instead, it provides strategy descriptions and research materials that you can use as a foundation for developing your own system.

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…

View on GitHub

The repository contains strategy concepts and research material for automated Polymarket trading systems and is primarily intended for educational and research purposes.

Telegram:
https://t.me/BenjaminCup

I Built a 5-Minute Polymarket Trading Bot — Live Trading Changed Everything

Benjamin-Cup — Tue, 22 Sep 2026 14:44:43 +0000

What live execution taught me about latency, liquidity, market regimes, risk management, and building real-time trading systems.

When I started building a 5-minute crypto trading bot for Polymarket, I thought the hardest problem would be prediction.

I was wrong.

I spent most of my early development time working on:

Momentum detection
Order-book imbalance
Volatility
Probability models
Short-term price movement
Entry signals

The backtests looked promising.

The signals looked accurate.

The strategy seemed straightforward:

Predict the next few minutes better than the market, enter the trade, and let the edge do the rest.

Then I put the system into live conditions.

That's when the real engineering problems appeared.

The biggest lesson wasn't about finding a better indicator.

It was about execution.

A strategy can be correct and still lose money if the execution is wrong.

This is what live testing taught me about building a short-horizon Polymarket trading system.

1. The Strategy Looked Good on Paper

The initial idea was simple.

If the bot could estimate the short-term direction of BTC or ETH accurately enough, there should be opportunities in 5-minute UP/DOWN markets.

So the first version focused heavily on:

Momentum
Order-book imbalance
Volatility breakouts
Short-term acceleration
Probability estimation

Backtesting produced encouraging results.

Signal accuracy looked good.

Returns appeared consistent.

The strategy seemed to validate the original hypothesis.

But there was an assumption underneath all of this:

If the prediction is correct, the trade should work.

Live trading exposed the weakness in that assumption.

A correct prediction isn't necessarily a profitable trade.

2. The First Surprise: The Signals Were Often Right

One of the first things I noticed was that the bot wasn't always wrong.

Sometimes the signal was actually correct.

The problem was that the system was too slow.

The bot could detect a BTC movement, but by the time the order reached the market:

The price had already moved
The available liquidity had changed
The spread had changed
The expected edge had decreased
The opportunity sometimes disappeared completely

The experience could be summarized as:

"I was right, but the market already knew it two seconds earlier."

That distinction is extremely important for automated trading.

There is a huge difference between:

Predicting a movement

and

getting a profitable fill before the market adjusts.

A model can have good predictive accuracy and still have poor trading performance.

3. Execution Became More Important Than the Signal

This was probably the biggest change in my thinking.

Two trades can receive exactly the same signal and produce completely different results.

Why?

Because the execution environment can be different.

For example:

Queue position
Spread
Order placement timing
Cancellation timing
Partial fills
Retry behavior
Available liquidity

can all affect the final result.

A strong signal with poor execution can lose money.

A weaker signal with excellent execution can sometimes produce a better outcome.

That changed the architecture of the system.

The bot was no longer just a prediction engine.

It was an execution system.

4. Latency Isn't a Constant

Another early assumption was that Polymarket could sometimes lag behind major spot exchanges.

That can create interesting opportunities.

But live conditions showed something more important:

The latency relationship changes.

Sometimes the difference was large enough to matter.

Sometimes it was almost gone.

Sometimes liquidity disappeared.

The environment could change from:

Useful latency difference
        ↓
Almost no difference
        ↓
Rapid repricing
        ↓
Liquidity disappears

This led to an important realization:

The opportunity isn't simply latency. It's changing latency.

A strategy based on:

"Market A is always slower than Market B"

is too simplistic.

A real system needs to continuously determine whether an exploitable difference exists right now.

5. Where the First Version Broke

After watching the system operate in live conditions, several failure points became obvious.

5.1 The Execution Engine

This became one of the biggest problems.

Orders could:

Fill too late
Remain exposed during cancellation
Receive partial fills
Enter after the edge disappeared
Execute at a worse price than expected

The signal engine wasn't necessarily the problem.

The execution engine was.

The market doesn't care that your model generated a perfect signal.

It cares about the price you actually receive.

5.2 Momentum Wasn't Enough

Momentum worked in certain environments.

But momentum alone generated false signals.

For example:

A breakout could fail immediately
A liquidity-driven spike could reverse
A short burst could disappear before market expiry

The original system sometimes treated momentum as directional truth.

Live trading changed that assumption.

A rapidly moving price doesn't automatically mean the movement will continue.

Momentum needs context.

6. Volatility Doesn't Automatically Mean Opportunity

Another early assumption was:

High volatility = good trading opportunity.

Live conditions made this much more complicated.

High volatility can come from very different situations:

Genuine directional expansion
Temporary liquidity imbalance
Short-lived price spikes
Liquidity vacuum conditions
Failed breakouts

The same volatility measurement can therefore represent completely different market environments.

This led to another design principle:

Volatility needs participation and liquidity context.

A volatility filter by itself isn't enough.

7. What Actually Worked

Not everything failed.

Some components remained useful when combined with the right market context.

Order-Flow Imbalance

Order-flow information became more useful when combined with confirmation.

For example:

Sustained bid pressure
Confirmation from the underlying spot market
Increasing volume delta
Corresponding movement in the prediction market

The important part wasn't one indicator.

It was confluence.

When several independent signals pointed in the same direction, the overall setup became more meaningful.

Instead of:

Momentum → BUY

the system moved toward something closer to:

Momentum
   +
Order Flow
   +
Spot Confirmation
   +
Liquidity
   ↓
Trade Decision

That was a much better mental model.

8. Trading Less Actually Helped

This was one of the most surprising lessons.

Initially, I wanted to find more opportunities.

More signals.

More trades.

More transactions.

But live testing showed that reducing the number of trades could be more valuable than adding another indicator.

The system became more selective around:

Flat markets
Unstable liquidity
Unclear directional regimes
Repeated entries
Low-quality setups

Instead of asking:

"Can I find another trade?"

the system started asking:

"Is this actually a good environment for trading?"

That's a completely different question.

9. Risk Management Had a Bigger Impact Than Expected

The initial version also relied on relatively static position sizing.

That wasn't ideal.

The system became more stable after introducing stricter controls.

These included:

Reduced exposure during unstable liquidity
Limits on consecutive trades
Cooldown periods
Trade-frequency caps
Drawdown protection
Exposure limits

None of these features made the prediction model smarter.

But they changed what happened when the model was wrong.

That's important.

A trading system doesn't need to eliminate losses.

It needs to control what happens when its assumptions fail.

10. The Biggest Discovery: Sometimes the Bot Shouldn't Trade

Eventually, one pattern became impossible to ignore.

The bot wasn't always losing because it predicted the market incorrectly.

Sometimes it was losing because it was trading when it shouldn't have been trading at all.

That changed the philosophy of the system.

Originally, the main question was:

"Is this a good signal?"

Later, the question became:

"Should the bot be active right now?"

That small change had a major architectural impact.

The system became less focused on generating more predictions and more focused on filtering bad environments.

11. The Architecture Changed

After several iterations, the system evolved into five major layers.

                 ┌─────────────────────┐
                 │   Market Data       │
                 │ Spot + Order Book   │
                 └──────────┬──────────┘
                            │
                            ▼
                 ┌─────────────────────┐
                 │ Market Regime       │
                 │ Filter              │
                 └──────────┬──────────┘
                            │
                    Trading Allowed?
                       /          \
                     NO            YES
                     │              │
                  NO TRADE          ▼
                            ┌─────────────────┐
                            │ Signal Engine   │
                            └────────┬────────┘
                                     │
                                     ▼
                            ┌─────────────────┐
                            │ Execution       │
                            │ Engine          │
                            └────────┬────────┘
                                     │
                                     ▼
                            ┌─────────────────┐
                            │ Risk Layer      │
                            └────────┬────────┘
                                     │
                                     ▼
                            ┌─────────────────┐
                            │ Post-Trade      │
                            │ Analytics       │
                            └─────────────────┘

Let's break down each layer.

12. Market Regime Filter

This became the first decision layer.

It evaluates things such as:

Volatility
Liquidity stability
Market participation
Current market conditions

Its job is simple:

Decide whether trading is allowed.

If the environment isn't suitable, the rest of the strategy doesn't matter.

This was an important architectural improvement.

Instead of:

Signal → Trade

the system became:

Market Environment
        ↓
Should We Trade?
        ↓
Signal
        ↓
Execution

13. Signal Engine

The signal engine became simpler.

Instead of adding dozens of indicators, I focused on a smaller group of signals:

Momentum acceleration
Order-flow imbalance
Cross-market confirmation

The entry threshold also became stricter.

The objective wasn't:

Generate as many signals as possible.

It was:

Generate fewer, higher-quality signals.

This reduced unnecessary activity and made the system easier to reason about.

14. Execution Engine

This layer received some of the biggest changes.

The system needed to become more aware of:

Current spread
Order placement
Cancellation behavior
Queue position
Changing liquidity
Fill conditions

The objective was no longer simply:

"Place the order."

It became:

"Place the order only when the expected execution still makes sense."

That's a significant difference.

The signal can remain valid while the trade itself becomes invalid.

For example:

Signal generated
       ↓
Market moves
       ↓
Liquidity changes
       ↓
Expected fill becomes worse
       ↓
Cancel trade

The ability to not execute is part of the execution engine.

15. Risk Layer

The risk layer became stricter as well.

It included:

Dynamic position sizing
Drawdown circuit breakers
Trade-frequency limits
Exposure controls
Cooldowns

The goal wasn't to eliminate losses.

That's unrealistic.

The goal was to prevent one bad market regime from turning into a chain of unnecessary trades.

For automated systems, controlling behavior during bad conditions can be just as important as optimizing behavior during good conditions.

16. Post-Trade Analytics

This became another important part of the architecture.

Every trade contains information.

So the system started tracking things such as:

Edge decay
Fill quality
Performance by market regime
Execution behavior
Strategy performance under different conditions

Instead of only asking:

"Did the trade win?"

the system could ask:

"Why did this trade behave the way it did?"

That creates a feedback loop:

Trade
  ↓
Execution Data
  ↓
Analytics
  ↓
Identify Failure Pattern
  ↓
Change Rules
  ↓
New Test
  ↓
Trade Again

This is much more useful than simply looking at total PnL.

17. The Hardest Lesson From 5-Minute Markets

After working on the system, I no longer think about a short-term prediction bot as simply a prediction model.

It's closer to a real-time decision system.

You are dealing with:

Prediction
    +
Timing
    +
Liquidity
    +
Execution
    +
Risk

A prediction can be correct while the trade is still bad.

A signal can work in one market regime and fail in another.

A strategy can look excellent in backtests and behave very differently once real execution enters the picture.

That's what makes short-horizon markets difficult.

The window for being right is extremely small.

18. The Real Edge May Be Knowing When Not to Trade

This became the biggest takeaway from the project.

Building a trading bot naturally creates the temptation to make it trade more.

More signals.

More indicators.

More opportunities.

More transactions.

But live execution taught me something different.

Sometimes the strongest decision is to do nothing.

The system became more selective rather than more aggressive.

That changed the entire design philosophy.

The goal isn't to trade every opportunity.

The goal is to participate only when the conditions justify participation.

For a short-horizon automated trading system, "no trade" is also a valid output.

19. What I Would Do Differently Today

If I were starting this project again, I would spend much less time optimizing the prediction model in isolation.

I would prioritize the engineering stack roughly like this:

Execution quality
Market-regime detection
Liquidity monitoring
Risk controls
Post-trade analytics
Signal generation

That's very different from where I started.

Initially, I thought:

Better Prediction
      ↓
Better Trading

Now I think about it more like:

Better Environment Detection
          +
Better Execution
          +
Better Risk Control
          +
Good Prediction
          ↓
Better Decision System

The prediction model is only one component.

20. Final Takeaways

After running and iterating on the system, these are the principles I keep coming back to:

Execution matters as much as prediction
Market edges are usually regime-dependent
Overtrading can be more damaging than weak signals
Liquidity behavior provides important information
Volatility needs context
Simple systems can be easier to control
Risk management matters when the model is wrong
Knowing when not to trade is part of the strategy

The biggest change wasn't a new indicator.

It was changing the question.

Instead of asking:

"Can I predict the next five minutes?"

I started asking:

"Is this a five-minute period where the system should participate at all?"

That question led to a much more selective architecture.

And, in my experience, that was far more important than adding another prediction signal.

The Code and Research

I've also been working on a public repository containing Polymarket trading-bot research, strategy concepts, and implementation ideas around short-term crypto markets.

The project covers areas including:

Polymarket trading strategies
Arbitrage concepts
TWAP strategies
5-minute crypto markets
15-minute crypto markets
Automated trading architecture

The repository is primarily intended for educational and research purposes rather than being presented as a complete production-ready trading system.

GitHub:

Benjam1nCup / Polymarket-trading-bot-python-V2

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…

View on GitHub

If you're building your own system, treat the strategies as starting points for research.

A backtested strategy should not be assumed to behave the same way in live markets.

Contact info:
Telegram https://t.me/BenjaminCup

Building a Market-Aware Execution Engine for Polymarket Bots with Python

Benjamin-Cup — Mon, 21 Sep 2026 14:39:51 +0000

How to build an adaptive execution layer that reacts to spread, liquidity, volatility, and order-book conditions.

A trading bot can have a good strategy and still produce poor execution.

Why?

Because finding a trading opportunity is only one part of the problem.

The bot also needs to decide:

How much should it buy?
Should it use a passive or aggressive order?
Is there enough liquidity?
Is the market moving too quickly?
Should a large order be split?
Should an existing order be cancelled or repriced?

This is where an adaptive execution layer becomes useful.

Instead of using a fixed rule such as:

Signal → Place Order

we can build:

Market Data
     ↓
Market Analyzer
     ↓
Risk Manager
     ↓
Adaptive Router
     ↓
Execution Engine
     ↓
Polymarket

The goal is not to predict the market better.

The goal is to make the execution decision more aware of current market conditions.

This article focuses on engineering and execution architecture. Adaptive execution does not guarantee profitability.

1. Why Execution Matters

Imagine the order book currently looks like this:

YES Bid:  $0.59
YES Ask:  $0.61

Spread:   $0.02

A strategy decides that buying YES is attractive.

But if the bot immediately sends a large aggressive order, it may consume liquidity across multiple price levels.

For example:

$0.61 → 300 shares
$0.62 → 500 shares
$0.63 → 800 shares
$0.64 → 1,000 shares

A 2,000-share order is therefore not necessarily executed at a single price.

The theoretical entry price and the actual execution price can be very different.

This means the execution system needs to understand more than just the current price.

It should monitor:

Spread
Liquidity
Order-book depth
Recent trades
Price movement
Volatility
Order size
Current position
Execution urgency

2. Adaptive Order Routing

A basic trading bot might contain logic like:

place_order(price, size)

The execution method is fixed.

An adaptive router introduces another decision layer:

                Market Data
                     ↓
              Market Analyzer
                     ↓
              Adaptive Router
                     ↓
        ┌────────────┼────────────┐
        ↓            ↓            ↓
     Passive      Aggressive     Split
        │            │            │
        └────────────┼────────────┘
                     ↓
              Execution Engine
                     ↓
                 Polymarket

The router can choose between:

PASSIVE
AGGRESSIVE
SPLIT
WAIT
CANCEL_REPRICE

The important architectural principle is:

The trading signal should decide whether there is an opportunity. The execution layer should decide how to interact with the order book.

3. Building the Market Analyzer

The first component is a market-state analyzer.

A simplified version could calculate the best bid, best ask, spread, and midpoint:

def market_metrics(orderbook):
    bid = orderbook["best_bid"]
    ask = orderbook["best_ask"]

    spread = ask - bid
    mid = (bid + ask) / 2

    return {
        "bid": bid,
        "ask": ask,
        "spread": spread,
        "mid": mid,
    }

A production implementation would maintain considerably more state:

best_bid
best_ask
spread
mid_price
bid_depth
ask_depth
imbalance
recent_volume
recent_trades
price_velocity
volatility

This state becomes the input for the routing decision.

4. Measuring Order-Book Liquidity

Order size should be compared with available liquidity.

For example:

def calculate_depth(book):
    total_bid = sum(
        level["size"]
        for level in book["bids"]
    )

    total_ask = sum(
        level["size"]
        for level in book["asks"]
    )

    return {
        "bid_depth": total_bid,
        "ask_depth": total_ask
    }

However, total depth alone isn't enough.

Consider:

$0.60 → 300 shares
$0.61 → 500 shares
$0.62 → 800 shares
$0.63 → 1,000 shares

A router should understand where the liquidity exists.

For a large order, consuming several levels can significantly change the average execution price.

So an execution engine should estimate the potential cost of walking through the order book before sending a large order.

5. Detecting Market Volatility

The router should also know whether the market is relatively stable or moving rapidly.

A simplified example:

import numpy as np

def calculate_volatility(prices):
    returns = np.diff(prices)
    return np.std(returns)

In a real system, the calculation should use a properly defined time series and sampling period.

The important concept is maintaining a short-term market regime.

For example:

LOW VOLATILITY
      ↓
More passive execution may be possible


HIGH VOLATILITY
      ↓
Re-evaluate more frequently

The exact behavior depends on the trading strategy.

6. Creating the Routing Decision

Now we can combine the market metrics.

A simple router might look like this:

def choose_route(spread, volatility):

    if spread > 0.03:
        return "PASSIVE"

    if volatility > 0.05:
        return "SPLIT"

    return "AGGRESSIVE"

This is intentionally simple.

A production router could incorporate:

Spread
+
Liquidity
+
Volatility
+
Order Size
+
Position Exposure
+
Signal Strength
+
Execution Urgency

The result could be:

PASSIVE
AGGRESSIVE
SPLIT
WAIT
CANCEL_REPRICE

The thresholds should be treated as strategy-specific parameters rather than universal trading values.

7. Turning the Router into a Python Class

A dedicated class makes the execution layer easier to extend:

class AdaptiveRouter:

    def __init__(self):
        pass

    def evaluate(self, spread, volatility):

        if spread > 0.03:
            return "PASSIVE"

        if volatility > 0.05:
            return "SPLIT"

        return "AGGRESSIVE"


router = AdaptiveRouter()

decision = router.evaluate(
    spread=0.02,
    volatility=0.01
)

print(decision)

Output:

AGGRESSIVE

Again, the numbers here are only examples.

The important part is the architecture, not the specific thresholds.

8. Splitting Large Orders

Suppose the strategy wants to buy:

2,000 shares

Instead of submitting everything at once:

BUY 2,000

the router could divide the parent order:

BUY 500
BUY 500
BUY 500
BUY 500

A simple implementation:

def split_order(total_size, chunks):

    chunk_size = total_size / chunks

    return [chunk_size] * chunks

Example:

orders = split_order(
    total_size=2000,
    chunks=4
)

print(orders)

Result:

[500, 500, 500, 500]

But splitting an order doesn't automatically improve execution.

The important part is what happens between child orders.

A better execution loop is:

Parent Order
     ↓
Child Order #1
     ↓
Re-evaluate Market
     ↓
Child Order #2
     ↓
Re-evaluate Market
     ↓
Child Order #3
     ↓
Re-evaluate Market
     ↓
Child Order #4

If market conditions change, the router can change the remaining execution plan.

9. Dynamic Repricing

Another useful component is stale-order management.

Suppose the bot submits:

BUY @ $0.59

Then the market moves:

Bid → $0.60
Ask → $0.62

The execution engine needs to decide whether the original order is still appropriate.

Possible questions include:

Should we keep the order?

Should we cancel it?

Should we reprice it?

Has the trading signal disappeared?

Has volatility increased?

Has liquidity changed?

A simple architecture:

Existing Order
      ↓
Market Changed?
      ↓
   ┌──┴──┐
   │     │
  YES    NO
   │     │
   ↓     ↓
Re-evaluate
           Keep

This prevents the bot from blindly leaving stale orders in a changing market.

10. Risk Management Comes First

The router shouldn't be allowed to bypass risk controls.

For example:

MAX_POSITION = 5000

if current_position >= MAX_POSITION:
    stop_trading()

A daily loss limit could be implemented as:

MAX_DAILY_LOSS = 1000

if daily_pnl <= -MAX_DAILY_LOSS:
    stop_trading()

Liquidity can also act as a filter:

if book_depth < minimum_depth:
    skip_trade()

And a volatility filter could prevent execution during extreme conditions:

if volatility > volatility_threshold:
    stop_trading()

A clean hierarchy is:

Signal
   ↓
Risk Manager
   ↓
Adaptive Router
   ↓
Execution Engine

The router decides how to execute only after the risk layer determines that trading is allowed.

11. Connecting Everything Together

A larger Polymarket bot can be structured like this:

┌────────────────────┐
│    Signal Engine   │
└─────────┬──────────┘
          ↓
┌────────────────────┐
│    Risk Manager    │
└─────────┬──────────┘
          ↓
┌────────────────────┐
│  Adaptive Router   │
└─────────┬──────────┘
          ↓
┌────────────────────┐
│  Execution Engine  │
└─────────┬──────────┘
          ↓
       Polymarket

This separation provides a major engineering advantage.

You can improve the execution system without rewriting the strategy.

For example:

Router V1
Simple spread rules

        ↓

Router V2
Liquidity-aware execution

        ↓

Router V3
Liquidity
+ volatility
+ imbalance
+ execution-cost model

The signal engine can remain unchanged.

12. A Practical Example

Suppose the market currently has:

YES Bid: 0.59
YES Ask: 0.61

Spread: 0.02

The strategy wants:

2,000 shares

The market analyzer reports:

Spread:      Moderate
Liquidity:   Moderate
Volatility:  Low
Position:    Within Limit

The router might choose:

Execution Mode: SPLIT

Then:

500 shares
     ↓
Re-evaluate
     ↓
500 shares
     ↓
Re-evaluate
     ↓
500 shares
     ↓
Re-evaluate
     ↓
500 shares

If the market changes significantly after the first execution, the router doesn't have to blindly continue with the original plan.

This is the core idea:

Don't decide the entire execution path once. Re-evaluate as market conditions change.

13. Measuring Execution Quality

An adaptive router should be evaluated using execution data, not assumptions.

Useful metrics include:

Fill Rate

Filled Orders
─────────────
Submitted Orders

Average Execution Price

Compare the expected entry price with the actual volume-weighted execution price.

Slippage

Actual Execution Price
        -
Reference Price

Execution Latency

Measure:

Signal
  ↓
Order Submitted
  ↓
Order Filled

Partial Fill Rate

How frequently are orders only partially filled?

Cancellation Rate

A router that constantly cancels and replaces orders may create unnecessary execution overhead.

Realized PnL

Execution metrics should ultimately be analyzed together with the strategy's overall performance.

For example, improving fill rate isn't necessarily useful if the average execution price becomes significantly worse.

14. Logging Execution Data

One of the most important parts of developing an adaptive router is collecting enough data to analyze its decisions.

A useful execution record might contain:

timestamp
market
side
signal_price
submitted_price
executed_price
order_size
filled_size
spread
depth
volatility
latency
PnL

This makes it possible to compare different routing policies.

For example:

Strategy A
Passive Execution

vs.

Strategy B
Aggressive Execution

vs.

Strategy C
Adaptive Execution

The comparison shouldn't focus only on historical PnL.

Also examine:

Slippage
Fill probability
Execution latency
Drawdown
Partial fills
Cancellation rate
Market conditions
Parameter sensitivity

This helps determine whether an observed improvement is robust or specific to a particular historical period.

15. Production Architecture

A more complete execution architecture could eventually look like:

                    Market Data
                         │
                         ▼
                  ┌──────────────┐
                  │ Market State │
                  └──────┬───────┘
                         │
          ┌──────────────┼──────────────┐
          ▼              ▼              ▼
       Spread         Liquidity      Volatility
          │              │              │
          └──────────────┼──────────────┘
                         ▼
                  ┌──────────────┐
                  │ Risk Manager │
                  └──────┬───────┘
                         │
                         ▼
                  ┌──────────────┐
                  │    Router    │
                  └──────┬───────┘
                         │
              ┌──────────┼──────────┐
              ▼          ▼          ▼
           Passive    Aggressive   Split
              │          │          │
              └──────────┼──────────┘
                         ▼
                  ┌──────────────┐
                  │  Execution   │
                  │    Engine    │
                  └──────┬───────┘
                         ▼
                     Polymarket

Each component has one primary responsibility:

Market Data
→ Collect information

Market State
→ Maintain current conditions

Signal Engine
→ Identify opportunities

Risk Manager
→ Decide whether trading is allowed

Adaptive Router
→ Decide how to execute

Execution Engine
→ Submit and manage orders

Analytics
→ Measure results

This modularity makes the system much easier to test and evolve.

16. Final Thoughts

A trading bot is more than a signal generator.

There is an entire execution layer between the strategy and the market.

A fixed system might do:

Signal
  ↓
Order

A more advanced system can do:

Signal
  ↓
Risk
  ↓
Market Analysis
  ↓
Adaptive Routing
  ↓
Order Management
  ↓
Performance Analytics

The router can continuously evaluate:

Spread
Liquidity
Volatility
Order Size
Position
Market State
Execution Urgency

and adjust the execution process accordingly.

The interesting engineering challenge isn't simply making the router more complicated.

It's collecting enough execution data to answer a much more important question:

Which execution decisions actually improve execution under different market conditions?

That is where an execution layer moves from a collection of rules toward a measurable trading-system component.

Open-Source Reference

I maintain a Polymarket trading-bot project where I experiment with trading strategies, execution logic, and related infrastructure.

GitHub:
https://github.com/Benjam1nCup/Polymarket-trading-bot-python-V2

The repository is intended for educational and research purposes. Trading involves substantial risk, and historical or simulated results do not guarantee future performance.

Connect

If you're interested in discussing:

Polymarket trading bots
Web3 development
Execution systems
Quantitative trading infrastructure
Automated trading architecture

You can find me on Telegram:

@BenjaminCup

This article is for educational purposes only and is not financial advice. Automated trading involves significant risks, including execution losses, liquidity risk, technical failures, and market volatility.

How a Polymarket Momentum Bot Calculates Position Size

Benjamin-Cup — Sat, 19 Sep 2026 14:53:32 +0000

From BTC momentum signals to dynamic share sizing

In automated prediction-market trading, detecting a momentum signal is only half of the problem.

The next question is:

How many shares should the bot actually trade?

In this video, you can see the bot operating in real time.

In this article, I’ll explain the position-sizing architecture behind a Polymarket momentum-arbitrage bot, including the active fixed-clip system and the more advanced Signal Strength (SigS) model.

GitHub: [Polymarket Trading Bot Python V2]

Benjam1nCup / Polymarket-trading-bot-python-V2

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…

View on GitHub

1. Two Position-Sizing Systems

The codebase contains two different approaches.

Active: Clip Engine

The current execution engine primarily uses fixed-size clips:

size = min(
    clip_shares,
    room_shares,
    room_usdc / ask
)

With a default clip of 20 shares, the bot can still reduce the order if:

inventory is near its limit
available USDC is insufficient
the current ask is expensive

For example:

clip size       = 20
inventory room  = 15
USDC room       = $10
ask             = $0.60

USDC capacity = 10 / 0.60 = 16.67

final size = min(20, 15, 16.67)
           = 15 shares

So the active system prioritizes capital and inventory controls rather than scaling directly with momentum strength.

2. How Momentum Controls the Side

Although the clip size is mostly fixed, BTC's relationship to the strike price determines which outcome receives more allocation.

Conceptually:

target_up_ratio =
clamp(
    0.5 +
    tilt_extra_pct *
    tanh((spot - strike) / sensitivity),
    0.35,
    0.65
)

Therefore:

BTC > strike → favor UP
BTC < strike → favor DOWN

The bot tries to buy the underweight side first.

The strategy also changes behavior depending on the remaining market time:

Phase	Approx. behavior
Early	~20-share clips
Expensive pair	Tilt toward favored side
Late window	Smaller favorite/hedge clips

3. The Advanced SigS Model

The more interesting position-sizing system is the Signal Strength (SigS) architecture.

Instead of using a fixed number of shares, it evaluates six market factors:

Factor	Weight
Momentum	25%
Cross-exchange agreement	15%
Order-book imbalance	20%
Liquidity	15%
Token price	15%
Market activity	10%

These factors produce a composite score between:

0.0 → 1.0

The architecture becomes:

BTC / Order Book Data
        ↓
Momentum Detection
        ↓
6-Factor Composite Score
        ↓
Signal Strength
        ↓
Share Calculation
        ↓
Risk Limits
        ↓
Execution

4. Momentum Detection

The strategy evaluates BTC movement across multiple timeframes.

A simplified UP signal requires:

short-term BTC move > threshold
AND
longer-term BTC move > threshold
AND
order-book confirmation

The momentum component normalizes the price movement:

short_norm =
min(abs(coin - prev_coin) / 0.1, 1.0)

long_norm =
min(abs(prev_coin - prev_1s) / 1.0, 1.0)

Then:

momentum_score =
    0.6 × short_norm +
    0.4 × long_norm

This gives greater importance to the immediate BTC move while still considering the previous movement.

5. Order-Book Imbalance

The bot also examines liquidity across the top levels of the Polymarket order book.

A simplified imbalance calculation is:

imbalance =
(bid_depth - ask_depth)
/
(bid_depth + ask_depth)

This helps determine whether the order book is confirming or contradicting the BTC momentum signal.

The strategy also considers:

top-five-level liquidity
token price
book update activity
Coinbase/Binance agreement

6. From Composite Score to Shares

The composite score is converted into a signal-strength multiplier:

sig_s =
    sig_s_min +
    composite ×
    (sig_s_max - sig_s_min)

Default parameters:

sig_s_min = 0.3
sig_s_max = 1.5
base_shares = 20

Therefore, the theoretical position range is:

20 × 0.3 = 6 shares

20 × 1.5 = 30 shares

So the dynamic sizing model can produce approximately:

6 → 30 shares

before risk and execution constraints.

7. Example

Suppose the composite score is:

0.85

Then:

sig_s =
0.3 + 0.85 × (1.5 - 0.3)

= 1.32

With 20 base shares:

shares = 20 × 1.32
       = 26.4

A weaker signal might produce:

composite = 0.20

sig_s = 0.54

shares = 20 × 0.54
       = 10.8

The important idea is:

Not every momentum signal receives the same position size.

The desired size reflects the broader market state.

8. Two-Leg Execution

The advanced strategy uses a two-leg structure.

Leg 1

Enter on the momentum side:

BTC momentum ↑
      ↓
UP signal
      ↓
Buy UP

Leg 2

After a BTC retracement condition:

BTC retracement
      ↓
Buy opposite side

The same calculated share quantity can be used for both legs.

The hedge trigger uses a dynamic distance:

diff =
max(
    abs(crypto_price - price_to_beat) × 0.57,
    5.2
)

This combines a proportional threshold with a minimum distance.

9. Signal Size Is Not Final Order Size

A critical design principle is that the SigS result should be treated as a requested position size, not an unconditional order size.

For example:

SigS requested = 30 shares
Inventory room = 18
Budget capacity = 15

Final executable size = 15

The execution layer should still enforce:

maximum inventory
available USDC
market liquidity
order state
stale-data checks
remaining market time

The architecture is therefore:

Signal Size
    ↓
Risk Limits
    ↓
Inventory Limits
    ↓
Budget Limits
    ↓
Final Order Size

10. Active vs Advanced System

The distinction is important.

	Clip Engine	SigS
Status	Active	In code, not wired into live engine
Size	Mostly fixed	Dynamic
Base size	~20	20
Momentum affects size	No	Yes
Momentum affects side	Yes	Yes
Composite scoring	No	Yes
Dynamic range	Limited by caps	~6–30 shares
Risk controls	Yes	Should be applied before execution

So it would be inaccurate to say that the current bot dynamically changes its share size on every momentum event.

The more precise description is:

The active bot uses fixed clips with inventory, budget, timing, and directional-tilt controls, while the codebase also contains a more advanced SigS architecture for dynamic momentum-based position sizing.

11. Final Architecture

The complete concept looks like:

Market Data
    ↓
Momentum Detection
    ↓
Signal Validation
    ↓
6-Factor Scoring
    ↓
Signal Strength
    ↓
Requested Shares
    ↓
Risk / Inventory Limits
    ↓
Order Execution
    ↓
Hedge / Second Leg

The interesting part isn't the final multiplication:

shares = base_shares × signal_strength

The important part is how the signal strength is constructed from:

momentum + exchange confirmation + order-book structure + liquidity + token price + market activity.

That creates a position-sizing framework that can respond to changing market conditions instead of treating every signal identically.

Explore the Code

The implementation and related research code are available here:

[GitHub — Polymarket Trading Bot Python V2]

Benjam1nCup / Polymarket-trading-bot-python-V2

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…

View on GitHub

For technical discussion, collaboration, or questions:

Telegram: [@BenjaminCup]
Contact: Telegram: https://t.me/BenjaminCup

Educational and research content only. This is not financial advice, and trading automated prediction-market strategies involves significant risk.

Building a Polymarket TWAP Momentum Price-Field Bot

Benjamin-Cup — Fri, 18 Sep 2026 16:29:48 +0000

Using token momentum, market structure, and TWAP timing to detect potential entries

Polymarket's move toward TWAP-based resolution changes how short-duration crypto markets can be analyzed.

Instead of simply asking:

"Is YES trading at $0.70?"

a trading system can ask:

Why did YES move to $0.70, and does the underlying market support that movement?

This is the idea behind a TWAP Momentum Price-Field Bot.

The Strategy

The bot continuously maintains a real-time state for each active market:

YES / NO price
Price momentum
Price velocity
Order-book depth
Bid/ask imbalance
Underlying price
TWAP state
Time remaining
Spread
Liquidity

The goal is to detect a directional price field where multiple signals align.

For example:

YES

0.58 → 0.61 → 0.65 → 0.68 → 0.71

Underlying:      UP
TWAP state:       UP
Order book:       Buyer-supported
Momentum:         Strong
Time remaining:   35s

Rather than buying simply because YES crossed 0.65, the bot evaluates whether the movement is supported by the broader market state.

Momentum Calculation

A basic momentum calculation can be:

momentum = current_price - price_n_seconds_ago

Velocity adds the time dimension:

velocity = (
    current_price - previous_price
) / time_difference

The system can also measure momentum persistence.

For example:

0.56
0.59
0.62
0.65
0.68
0.70

is a very different signal from:

0.61
0.66
0.62
0.67
0.63
0.65

The first sequence shows more consistent directional movement.

Example Signal

A simplified signal engine might look like:

def momentum_signal(market):

    if market.token_price < 0.65:
        return False

    if market.momentum < MIN_MOMENTUM:
        return False

    if market.momentum_duration < MIN_DURATION:
        return False

    if not market.underlying_confirms:
        return False

    if not market.twap_confirms:
        return False

    if market.spread > MAX_SPREAD:
        return False

    if market.liquidity < MIN_LIQUIDITY:
        return False

    return True

The 0.65 level is only a filter. The actual signal comes from the combination of momentum, underlying data, TWAP state, order-book conditions, and timing.

Price Field Architecture

A practical implementation can separate the system into several services:

Polymarket WebSocket
        ↓
Market State Engine
        ↓
Momentum Calculator
        ↓
TWAP / Underlying Data
        ↓
Signal Engine
        ↓
Risk Manager
        ↓
Execution Engine

The market-state engine maintains the latest information for each active slug, while the signal engine evaluates whether the current state satisfies the trading conditions.

This separation also makes backtesting and debugging easier.

Why Time Matters

The same token price can represent very different situations depending on the remaining time.

YES = 0.70

4 minutes remaining
        vs
20 seconds remaining

A momentum strategy therefore needs to include time remaining as part of the signal.

The appropriate time windows and thresholds should come from historical testing rather than being hard-coded from assumptions.

Order-Book Confirmation

Token momentum becomes more useful when combined with market structure.

The bot can monitor:

Bid volume
Ask volume
Order-book imbalance
Depth near mid-price
Spread
Recent fills
Liquidity

For example:

YES = 0.69

Bids:
0.69 → 120
0.68 → 180
0.67 → 240

Asks:
0.70 → 30
0.71 → 45
0.72 → 60

A relatively thin ask side can allow aggressive buying to move the token quickly through several price levels.

However, the bot still needs to account for slippage and the possibility that the visible book changes before execution.

Risk Management

Momentum can reverse quickly, so execution should be separated from risk management.

Important controls include:

Maximum position size
Maximum market exposure
Maximum slippage
Maximum spread
Daily loss limit
Stale-data detection
WebSocket disconnect protection
Execution timeout
Price-reversal detection

Stale-data protection is especially important. If the underlying feed stops updating while the Polymarket order book continues changing, the system should stop treating the signal as valid.

Backtesting

For each historical market, I would record:

Timestamp
YES / NO price
Underlying price
TWAP
Order book
Momentum
Time remaining
Signal
Entry price
Settlement result
Slippage
PnL

Then test different thresholds and market regimes.

The objective is not to find a perfect number such as:

YES > 0.673
Momentum > 0.084

The objective is to determine whether the signal remains robust outside the dataset used to develop it.

A useful development process is:

Historical backtest
        ↓
Market replay
        ↓
Paper trading
        ↓
Small live test
        ↓
Execution analysis
        ↓
Iteration

The Core Idea

The TWAP Momentum Price-Field Bot combines several layers of market information:

Token Momentum
      +
Underlying Price
      +
TWAP State
      +
Order-Book Structure
      +
Time Remaining
      +
Execution Quality
      +
Risk Management

The goal is not to predict every market correctly.

It is to systematically identify situations where the market is repricing quickly and determine whether that movement is supported by the underlying data and market structure.

That makes the strategy a useful framework for researching short-duration TWAP markets and building systematic trading infrastructure around them.

Explore the Code

I also maintain a Polymarket trading-bot repository for educational and development reference:

GitHub:

Benjam1nCup / Polymarket-trading-bot-python-V2

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…

View on GitHub

For development, collaboration, or technical discussion:

Telegram:
https://t.me/BenjaminCup

Building a Polymarket TWAP Trading Bot: Using Coinbase for Real-Time Risk Management

Benjamin-Cup — Thu, 17 Sep 2026 14:37:55 +0000

Building a Polymarket TWAP Trading Bot: Using Coinbase for Real-Time Risk Management

How an external Coinbase price feed can help a Polymarket trading bot detect rapid market changes and manage open positions

Automated trading is not only about finding a good entry.

In short-duration crypto markets, the market can change significantly only a few seconds after a position is opened.

That creates a second problem:

What should the bot do when the conditions behind the original trade start changing?

This is the problem I have been working on with a Polymarket TWAP trading bot.

The system combines Polymarket market data with Coinbase real-time price movement to create an additional risk-management layer.

The goal is not to use Coinbase to predict the final Polymarket outcome.

Instead, Coinbase acts as an external market-movement signal that can help the bot recognize potentially dangerous conditions faster.

1. The Problem With Entry-Only Strategies

A simple trading bot might look like this:

Market Data
    ↓
Signal
    ↓
BUY
    ↓
Wait for Resolution

This is easy to understand, but it ignores what happens after the position is opened.

A more complete system looks like:

Market Data
    ↓
Signal
    ↓
BUY
    ↓
Monitor Position
    ↓
Risk Detection
    ↓
HOLD / REDUCE / EXIT

This difference becomes especially important in short-duration markets.

A trade that looked attractive five seconds ago may have a completely different risk profile now.

2. Understanding the Role of TWAP

Polymarket crypto markets can use defined resolution rules and specific resolution sources. The market metadata exposes fields such as resolutionSource, so a trading system should treat the resolution mechanism as part of its market state rather than assuming that any external spot price is the settlement value.

This creates an important distinction:

External Exchange Price
        ↓
Market Information

Polymarket Market
        ↓
Trading Price

Resolution Mechanism
        ↓
Final Outcome

These are related, but they are not necessarily identical.

Therefore, I don't use Coinbase as a replacement for the actual resolution source.

I use it as another data stream.

3. Why Coinbase?

Coinbase can provide a fast view of movement in the underlying crypto market.

For example, the bot can monitor:

BTC Price
Price Change
Short-Term Momentum
Volatility
Direction

Imagine the bot has already purchased an UP token.

Then Coinbase suddenly shows a significant downward movement.

The bot can detect:

Coinbase
BTC starts moving down
        ↓
Risk Signal
        ↓
Check Polymarket Position
        ↓
Evaluate Liquidity
        ↓
HOLD / REDUCE / EXIT

The important point is that Coinbase is not making the trading decision by itself.

It is one input into the risk engine.

4. External Data vs. Polymarket Data

I think about the system as two separate views of the market.

Polymarket

The bot monitors:

UP/DOWN token prices
Order book
Bid/ask
Liquidity
Market status
Time remaining
Current position

Polymarket's CLOB APIs provide market price information and historical price data that can be incorporated into this state.

Coinbase

The bot monitors:

BTC price
Short-term price changes
Momentum
Volatility
Rapid movements

The two feeds answer different questions.

Polymarket
"What is happening inside the prediction market?"

Coinbase
"What is happening in the underlying crypto market?"

Combining those views can give the risk engine more information.

5. Detecting a Risk Event

A basic implementation can calculate short-term price movement.

For example:

price_change = current_price - previous_price

price_change_pct = (
    price_change / previous_price
) * 100

But one price update isn't necessarily useful.

Instead, the bot can monitor multiple windows:

1 second
3 seconds
5 seconds
10 seconds
30 seconds

For example:

BTC

1s   → -0.03%
3s   → -0.08%
5s   → -0.17%
10s  → -0.21%

The risk engine can interpret persistent movement differently from random noise.

6. Risk Score

Instead of creating one simple rule like:

if btc_change < -0.20:
    sell()

I prefer thinking in terms of a risk score.

For example:

Coinbase movement
        +
Polymarket movement
        +
Order-book condition
        +
Position size
        +
Volatility
        +
Time remaining
        ↓
    Risk Score

Then:

Low Risk
   ↓
HOLD

Medium Risk
   ↓
REDUCE

High Risk
   ↓
EXIT

This creates a much more flexible risk-management architecture.

7. Example: Position Management

Suppose the bot owns:

100 UP tokens

Then the external market suddenly moves against the position.

The risk engine could respond in stages.

Low risk

Risk Score = 20

Action:
HOLD

The bot continues monitoring.

Medium risk

Risk Score = 65

Action:
REDUCE

Sell 30 tokens

The bot reduces exposure without completely abandoning the position.

High risk

Risk Score = 90

Action:
EXIT

Close remaining position

The exact thresholds should be determined through testing rather than assuming that a particular number is universally effective.

8. Why Not Just Use a Stop Loss?

A traditional stop loss is useful, but it only looks at the position's price.

A risk engine can consider more information.

For example:

Stop Loss

Token price
     ↓
Exit

versus:

Risk Engine

Coinbase movement
       +
Polymarket price
       +
Order book
       +
Liquidity
       +
Position exposure
       +
Time remaining
       ↓
Decision

This allows the system to react to changes in market conditions before relying exclusively on the token price.

However, this also introduces additional model complexity and the possibility of false signals.

9. Avoiding Market Noise

This is one of the hardest parts.

Crypto markets move constantly.

If the bot reacts to every small movement, it may start over-trading.

For example:

BTC

+0.04%
-0.05%
+0.03%
-0.06%
+0.02%

A naive risk engine could continuously generate:

RISK
SAFE
RISK
SAFE
RISK

That is not useful.

The system needs filtering.

Possible techniques include:

Thresholds

Ignore very small movements.

Time windows

Measure movement over several seconds rather than individual ticks.

Confirmation

Require multiple signals before changing the position.

Position-aware risk

Use different sensitivity depending on the size of the position.

Time-to-expiration

Change risk sensitivity as the market approaches its end.

10. Position Size Changes the Risk

The same price movement can have very different consequences depending on position size.

Consider:

Account: $5,000

Position A: $25
Position B: $1,000

A small market movement is much more important when the position represents a large percentage of the account.

Therefore, the risk engine can consider:

exposure_ratio = position_value / account_value

Then the system can become more defensive when exposure increases.

Conceptually:

Higher Exposure
      ↓
Higher Risk Sensitivity

11. The Architecture

The architecture I am experimenting with looks approximately like this:

                  ┌─────────────────┐
                  │    Coinbase     │
                  │   Price Feed    │
                  └────────┬────────┘
                           │
                           ▼
                  ┌─────────────────┐
                  │  Risk Signal    │
                  │     Engine      │
                  └────────┬────────┘
                           │
                           ▼
┌─────────────────┐ ┌─────────────────┐
│   Polymarket    │→│ Position Manager│
│ Market Data     │ └────────┬────────┘
└─────────────────┘          │
                             ▼
                     ┌───────────────┐
                     │   Decision    │
                     └───────┬───────┘
                             │
                    ┌────────┼────────┐
                    ▼        ▼        ▼
                   HOLD    REDUCE    EXIT

The important design principle is separation.

The strategy does not need to manage every risk decision.

12. Strategy vs. Risk Management

This distinction is important.

Strategy

The strategy asks:

"Is there an opportunity?"

For example:

Order book
Momentum
TWAP state
Probability
Liquidity
       ↓
BUY

Risk Engine

The risk engine asks:

"Should we continue holding this position?"

For example:

Coinbase movement
Polymarket movement
Volatility
Liquidity
Exposure
Time remaining
       ↓
HOLD / REDUCE / EXIT

This separation makes the system easier to test.

13. A Simplified Risk Loop

The implementation can be structured around a continuous loop:

while position_is_open:

    coinbase_data = get_coinbase_state()

    polymarket_data = get_polymarket_state()

    exposure = get_position_exposure()

    volatility = calculate_volatility()

    risk_score = calculate_risk(
        coinbase_data,
        polymarket_data,
        exposure,
        volatility
    )

    if risk_score >= EMERGENCY_LEVEL:
        emergency_exit()

    elif risk_score >= REDUCE_LEVEL:
        reduce_position()

    else:
        continue_holding()

This is only a simplified example.

A real system also needs to handle:

stale data
WebSocket disconnects
reconnects
duplicate events
partial fills
rejected orders
insufficient liquidity
API failures
timing differences
incorrect market state

14. Latency Is Part of the System

A risk signal is only useful if the bot can process it and react appropriately.

The desired pipeline is:

Coinbase WebSocket
        ↓
Price Update
        ↓
Risk Calculation
        ↓
Position Check
        ↓
Decision
        ↓
Order Submission

The fewer unnecessary steps between the observation and decision, the easier it is to build a responsive system.

But speed alone isn't enough.

A faster system using stale or incorrect data is still making bad decisions faster.

Therefore:

Low latency and data quality have to be designed together.

15. Testing the Risk Engine

Before using a risk-management system with real capital, I would test the risk layer independently.

Historical replay

Feed historical market data into the risk engine.

Historical Data
      ↓
Risk Engine
      ↓
Simulated Decisions

Paper trading

Run the complete system without sending real orders.

Stress testing

Simulate sudden movements:

-0.10%
-0.30%
-0.50%
-1.00%

and measure how the risk engine responds.

Liquidity testing

Simulate a position that needs to be closed while the order book becomes thin.

Connection testing

Disconnect the Coinbase or Polymarket WebSocket and verify that the bot enters a safe state.

A production-oriented trading system should never assume that missing data means "nothing changed."

16. Coinbase Is Not a Prediction Oracle

This is the most important point of this approach.

I am not using Coinbase like this:

Coinbase goes down
       ↓
DOWN must win
       ↓
SELL everything

That would be an oversimplification.

Instead:

Coinbase moves rapidly
       ↓
Market conditions changed
       ↓
Re-evaluate current position
       ↓
Risk engine decides
       ↓
HOLD / REDUCE / EXIT

Coinbase becomes a risk-management input, not a prediction oracle.

17. The Bigger Engineering Idea

The interesting part of automated trading isn't only finding an entry.

A complete trading system needs to continuously answer:

What is happening?

What changed?

Does my original signal still make sense?

How much exposure do I have?

Is the market still liquid?

Should I continue holding?

Should I reduce?

Should I exit?

This changes the architecture from:

Signal → Trade

to:

Observe
   ↓
Analyze
   ↓
Enter
   ↓
Monitor
   ↓
Detect Risk
   ↓
Manage Position
   ↓
Exit

That is the architecture I am interested in building.

Open-Source Project

I am continuing to experiment with Polymarket trading infrastructure and automated trading strategies.

The project is available on GitHub:

GitHub:

Benjam1nCup / Polymarket-trading-bot-python-V2

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…

View on GitHub

The repository is intended for research, experimentation, and educational purposes.

If you're interested in Polymarket bots, automated trading systems, Web3 development, or trading infrastructure, feel free to connect.

Telegram: @BenjaminCup

https://t.me/BenjaminCup

Final Thoughts

The biggest lesson from building automated trading systems is that the entry signal is only one part of the problem.

A bot can identify an opportunity correctly and still encounter a rapidly changing market immediately afterward.

Using an external market feed such as Coinbase gives the risk engine another real-time view of market conditions.

The architecture becomes:

Polymarket Data
       +
Coinbase Data
       ↓
Risk Engine
       ↓
Position Management
       ↓
HOLD / REDUCE / EXIT

The objective isn't to predict every market movement.

It is to build a system that can observe changing conditions, reassess existing positions, and respond according to predefined risk rules.

That is where I believe a lot of the interesting engineering work in automated Polymarket trading exists.

polymarket #tradingbot #twap #crypto #web3 #python #algorithmictrading #riskmanagement #coinbase #automation

Building a Low-Latency Polymarket TWAP Final-Cycle Sniper Bot

Benjamin-Cup — Wed, 16 Sep 2026 15:10:40 +0000

Combining Chainlink TWAP, Coinbase Market Data, Order-Book Analysis, and Real-Time Execution

Polymarket's crypto Up/Down markets became a significantly more interesting engineering problem after the transition to TWAP-based resolution.

A simple strategy that watches the final spot price is no longer enough.

The settlement mechanism is based on the specified Chainlink TWAP, while external exchanges such as Coinbase can provide faster information about short-term market movements.

This creates an interesting systems problem:

Can we monitor multiple price feeds in real time, understand the remaining TWAP dynamics, detect a high-confidence end-cycle condition, and execute an order before the market resolves?

That is the idea behind the Polymarket TWAP Final-Cycle Sniper.

This article explains the architecture and strategy concept behind the bot.

1. What Is a TWAP Final-Cycle Sniper?

The basic idea is simple:

Coinbase Price
      │
      ▼
Fast Market Signal
      │
      ├──────────────┐
      │              │
      ▼              ▼
Chainlink/TWAP   Polymarket CLOB
      │              │
      └──────┬───────┘
             ▼
       Signal Engine
             │
             ▼
      Final-Cycle Check
             │
             ▼
       Risk Management
             │
             ▼
       Order Execution

The bot continuously monitors:

Coinbase price data
Chainlink-related market data
Polymarket market state
market strike/reference price
UP/DOWN token prices
order-book liquidity
time remaining
price movement and direction

When the market enters its final cycle and the available information indicates a highly asymmetric outcome, the bot can attempt to buy the expected winning token at a predefined target price.

One target used by this strategy concept is around $0.99.

The important point is that the strategy is not simply:

BTC > Strike → Buy UP

Instead, it combines price feeds + TWAP context + time + order-book conditions + execution constraints.

2. Why TWAP Changes the Trading Problem

With TWAP-based resolution, the final settlement is tied to a time-weighted Chainlink reference rather than simply taking the last exchange price.

That means there are several different prices to think about:

Coinbase
   │
   │
   ▼
External spot market

Chainlink
   │
   │
   ▼
Settlement data

TWAP
   │
   │
   ▼
Resolution reference

Polymarket
   │
   │
   ▼
Tradable UP/DOWN tokens

These values are related, but they are not interchangeable.

This distinction is fundamental to the bot's architecture.

3. Why Monitor Coinbase?

Coinbase is used as an additional external market-data source.

The reason is latency.

A fast exchange feed can react to market movement before the corresponding Chainlink-derived information fully reflects that movement.

For example:

T0

Coinbase       110,000
Chainlink      109,990

Then BTC moves rapidly:

T1

Coinbase       110,080
Chainlink      110,025

Coinbase has provided an early indication of the move.

The bot can use that information as a leading signal, while still treating Chainlink/TWAP as the relevant settlement context.

So the architecture is:

Coinbase
   ↓
Fast signal

Chainlink
   ↓
Settlement context

Polymarket
   ↓
Execution environment

This separation is important.

Coinbase does not determine the Polymarket outcome.

4. The End-Cycle Opportunity

The strategy focuses on the final portion of the market lifecycle.

Early in the market:

UP       $0.51
DOWN     $0.49

There is significant uncertainty.

Later:

UP       $0.85
DOWN     $0.15

Near expiration:

UP       $0.97+
DOWN     $0.03-

The closer the market gets to resolution, the more information becomes available about the eventual outcome.

The bot therefore becomes increasingly interested in:

time_remaining

For example:

time_remaining = market_end_time - current_time

Time is not just another variable.

It changes the meaning of every other variable.

A $100 price difference from the strike with three minutes remaining is a different situation from the same difference with three seconds remaining.

5. Core Data Model

A useful internal state object can look like:

market_state = {
    "market_id": "...",
    "strike": 110000.0,
    "coinbase_price": 110085.0,
    "chainlink_price": 110050.0,
    "twap": 110040.0,
    "time_remaining": 4.2,
    "up_bid": 0.97,
    "up_ask": 0.98,
    "down_bid": 0.02,
    "down_ask": 0.03,
}

The strategy engine should operate on a synchronized snapshot of this state.

That makes the system easier to debug and backtest.

6. Real-Time Data Pipeline

A latency-sensitive architecture should be event-driven rather than relying entirely on repeated REST polling.

Conceptually:

             ┌─────────────────┐
             │ Coinbase WS     │
             └────────┬────────┘
                      │
                      ▼
              ┌───────────────┐
              │ Price State   │
              └───────┬───────┘
                      │
                      │
┌─────────────────┐   │   ┌─────────────────┐
│ Chainlink Data  │───┼──►│ Signal Engine   │
└─────────────────┘   │   └────────┬────────┘
                      │            │
┌─────────────────┐   │            ▼
│ Polymarket CLOB │───┘       Risk Engine
└─────────────────┘                 │
                                    ▼
                              Order Executor

Each feed updates an internal state store.

The strategy engine then evaluates the latest synchronized information.

7. Coinbase + Chainlink Confirmation

A useful signal is not necessarily based on one price.

Instead, the bot can look for agreement.

For example:

Strike:       110,000

Coinbase:     110,115
Chainlink:    110,070

Time left:    5 seconds

The system sees:

Coinbase > strike
Chainlink > strike
Time remaining is very small

That is substantially different from:

Coinbase:     110,115
Chainlink:    109,920

Time left:    5 seconds

The second situation contains greater disagreement between the data sources.

A professional implementation should therefore model feed agreement/disagreement rather than treating every tick as a signal.

8. Signal Persistence

One of the simplest ways to reduce noise is to require the signal to persist.

Instead of:

one tick → trade

use something closer to:

multiple observations
        ↓
consistent direction
        ↓
signal confirmation
        ↓
trade evaluation

For example:

Time       Coinbase     Chainlink

-10s       110,070      110,020
-9s        110,080      110,030
-8s        110,085      110,035
-7s        110,095      110,040
-6s        110,110      110,050

This provides much more context than a single observation.

9. Order-Book Analysis

Prediction is only half of the problem.

Execution is the other half.

Suppose the bot decides that UP is the expected winner.

The order book might look like:

UP

$0.97    20 shares
$0.98    35 shares
$0.99    100 shares
$1.00    500 shares

If the strategy wants 500 shares at $0.99, there may simply not be enough available liquidity.

Therefore the bot must monitor:

best bid
best ask
spread
depth
available quantity
recent trades
estimated slippage

This is why a backtest using only candle prices can be misleading for a strategy like this.

10. The $0.99 Entry Concept

The strategy can define an entry target around:

$0.99

If the selected token resolves to $1.00, the theoretical gross difference is:

$1.00 - $0.99 = $0.01

For example:

1,000 shares   → $10 gross difference
10,000 shares  → $100 gross difference

But this is not guaranteed profit.

Real execution must account for:

Entry price
+ fees
+ slippage
+ failed fills
+ latency
+ losing signals
+ liquidity constraints

The strategy therefore needs an execution model rather than assuming every order will fill at the target price.

11. Fill Rate Is Not the Same as Signal Accuracy

This is an important distinction when evaluating the bot.

Imagine:

100 signals

92 correct outcomes
70 successful executions

Then:

Signal accuracy = 92%

Execution success = 70%

These metrics describe completely different parts of the system.

A useful dashboard should therefore track:

Signals
Correct signals
Submitted orders
Filled orders
Partially filled orders
Cancelled orders
Average entry
Average slippage
Realized PnL

This gives a much more realistic picture of strategy performance.

12. Stale Data Protection

Real-time trading systems need protection against stale feeds.

Consider:

Coinbase:       100 ms old
Polymarket:     50 ms old
Chainlink:      8 seconds old

Using all three values as if they were equally current could create a bad signal.

A simple safety check might be:

if now - chainlink_timestamp > MAX_DATA_AGE:
    reject_signal()

Similar checks can be applied to:

Coinbase
Chainlink
Polymarket
Order book
Market metadata

The bot should know not only what the latest value is, but also how old that value is.

13. Latency Budget

For a final-cycle strategy, the complete execution path matters.

Market movement
      ↓
Exchange update
      ↓
WebSocket
      ↓
Local processing
      ↓
Signal calculation
      ↓
Risk checks
      ↓
Order creation
      ↓
Network
      ↓
Polymarket CLOB
      ↓
Matching

Every stage consumes time.

This makes infrastructure part of the strategy.

Important engineering considerations include:

persistent WebSocket connections
low-latency VPS/networking
synchronized system clocks
efficient state updates
connection recovery
duplicate-message handling
order acknowledgement tracking
stale-data detection

14. Strategy Engine

A simplified strategy engine could look like:

def evaluate_market(state):

    if state.time_remaining > FINAL_CYCLE_THRESHOLD:
        return None

    if state.data_is_stale:
        return None

    direction = determine_direction(
        state.coinbase_price,
        state.chainlink_price,
        state.strike
    )

    if not direction:
        return None

    if not feeds_confirm(direction):
        return None

    if not sufficient_liquidity(direction):
        return None

    if not acceptable_execution_price(direction):
        return None

    return direction

Then execution becomes a separate responsibility:

direction = evaluate_market(state)

if direction:
    execute_order(direction)

Keeping signal generation and execution separate makes the system easier to test.

15. Risk Engine

A production bot should have a dedicated risk layer.

For example:

MAX_ORDER_SIZE
MAX_POSITION_SIZE
MAX_SLIPPAGE
MAX_DAILY_LOSS
MAX_DATA_AGE
MIN_LIQUIDITY
MIN_CONFIDENCE

The architecture should be:

Signal
  ↓
Risk Engine
  ↓
Approved?
  │
  ├── No  → Ignore
  │
  └── Yes → Execute

This is particularly important because a signal can be correct while the execution conditions are still unacceptable.

16. Handling Failed Execution

A serious bot must assume that orders can fail.

Possible cases include:

Order rejected
Order partially filled
Order not filled
Market moved
Connection lost
Data became stale
Market resolved

The execution engine therefore needs explicit state transitions.

For example:

SIGNAL_DETECTED
      ↓
ORDER_SUBMITTED
      ↓
 ┌────┴─────────┐
 ▼              ▼
FILLED       NOT_FILLED
 ▼              │
POSITION        ▼
            CANCEL/RETRY

Retry logic should be carefully bounded.

Blindly retrying an order during the final seconds can turn a missed opportunity into an unintended trade.

17. Backtesting

The strategy should be evaluated using historical market data.

A useful dataset might contain:

market_id
timestamp
strike
coinbase_price
chainlink_price
TWAP
UP bid
UP ask
DOWN bid
DOWN ask
order-book depth
time_remaining
signal
entry_price
fill_status
resolution
PnL

The critical rule is:

Only use information that would have been available at the exact moment the strategy made its decision.

Otherwise the backtest can introduce look-ahead bias.

18. Paper Trading

After historical testing, paper trading provides the next validation layer.

The architecture becomes:

Real Market Data
       ↓
Real Signal Engine
       ↓
Virtual Order
       ↓
Simulated Execution
       ↓
Market Resolution
       ↓
Performance Report

Paper trading can reveal problems that pure backtesting misses:

real-time latency
WebSocket disconnects
stale data
order-book changes
missed fills
signal timing
unexpected market behavior

19. Metrics

For this type of system, I would track at least these metrics.

Signal Accuracy

Correct signals / total signals

Fill Rate

Filled orders / submitted orders

Average Entry Price

Total execution cost / shares filled

Slippage

Actual execution price
-
Expected execution price

Execution Latency

Order submission timestamp
-
Signal timestamp

Net PnL

After applicable trading costs and execution effects.

Maximum Drawdown

The largest peak-to-trough decline in the strategy's equity curve.

These metrics should be analyzed independently.

20. Complete Strategy Flow

The complete Final Sniper pipeline can be summarized as:

                Market Discovery
                       │
                       ▼
                Market Metadata
                       │
                       ▼
              ┌─────────────────┐
              │ Real-Time Feeds │
              └────────┬────────┘
                       │
          ┌────────────┼────────────┐
          ▼            ▼            ▼
      Coinbase      Chainlink    Polymarket
          │            │            │
          └────────────┼────────────┘
                       ▼
                 State Engine
                       │
                       ▼
               Time Remaining
                       │
                       ▼
                 Signal Engine
                       │
                       ▼
              Final-Cycle Filter
                       │
                       ▼
                 Risk Engine
                       │
                       ▼
              Order-Book Check
                       │
                       ▼
                Order Executor
                       │
                       ▼
                 Fill Tracking
                       │
                       ▼
                  Resolution
                       │
                       ▼
                Performance Data

This is the architecture that turns a simple trading idea into a real trading system.

21. Repository

The implementation and related Polymarket trading-bot research are available here:

GitHub:

Benjam1nCup / Polymarket-trading-bot-python-V2

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…

View on GitHub

The repository is intended as an educational and development reference for experimenting with Polymarket automation, market data, strategy logic, and execution infrastructure.

22. What Makes This Strategy Interesting?

The interesting part is not simply:

"Buy the winning token for $0.99."

The engineering challenge is determining:

When is the outcome sufficiently asymmetric, when is the underlying data trustworthy, and can the order actually be executed at the required price before resolution?

That requires combining several systems:

Market Data
+
TWAP Understanding
+
Real-Time Streaming
+
Signal Processing
+
Order-Book Analysis
+
Risk Management
+
Low-Latency Execution

This is what makes the Final-Cycle Sniper an interesting project from a software-engineering perspective.

Conclusion

The TWAP transition changes how Polymarket crypto markets should be analyzed.

The settlement reference is important, but it is only one part of the trading system.

A practical automated strategy can combine:

Chainlink/TWAP context for understanding the settlement mechanism
Coinbase market data for faster external price information
Polymarket CLOB data for actual execution conditions
time remaining for end-cycle detection
order-book analysis for liquidity and slippage
risk controls for execution safety
low-latency infrastructure for time-sensitive orders

The result is a system designed around one specific question:

Can a trader identify a late-cycle asymmetric opportunity early enough to execute it under real market conditions?

That is the problem the Polymarket TWAP Final-Cycle Sniper is designed to investigate.

Disclaimer

This project is for educational and research purposes. Trading outcomes, fill rates, execution prices, and profitability are not guaranteed. Real-world performance depends on market conditions, liquidity, latency, competition, fees, and the behavior of the underlying settlement mechanism. Always perform independent testing and risk assessment before using real capital.

Contact

GitHub:
https://github.com/Benjam1nCup/Polymarket-trading-bot-python-V2

Telegram:
https://t.me/BenjaminCup

Building a Real-Time Coinbase BTC Price Feed for a Polymarket Momentum Bot

Benjamin-Cup — Tue, 15 Sep 2026 16:26:11 +0000

Part 2 — Turning real-time BTC price updates into a short-term momentum signal

In the previous part of this series, I explained how the introduction of 60-second TWAP settlement changed the architecture of my Polymarket crypto trading bots.

The key idea is that an external exchange such as Coinbase should not be treated as the settlement source.

You can view real-time footage of the bot in operation via this video link.

Instead, it can provide an early market signal.

When BTC moves quickly on an external exchange, that information can potentially appear before the corresponding movement is fully reflected in the Chainlink/TWAP-related state used by Polymarket.

So the next engineering problem is:

How do we turn a real-time BTC price stream into a useful short-term momentum signal?

That's what we'll build in this article.

The Architecture

The overall system looks like this:

Coinbase
    │
    ▼
WebSocket Price Feed
    │
    ▼
Market Data Processor
    │
    ▼
Short-Term Price History
    │
    ▼
Momentum Engine
    │
    ▼
Momentum Signal
    │
    ▼
Polymarket Decision Engine

This article focuses on everything up to the momentum signal.

The Polymarket decision layer comes later.

Why Real-Time Data?

For a short-duration trading strategy, the latest BTC price isn't enough.

Consider:

BTC = $100,000

Then:

$100,000
    ↓
$100,040
    ↓
$100,090
    ↓
$100,150

If we only request the latest price periodically, we may see:

BTC = $100,150

But we lose information about how the market got there.

A real-time stream gives us the sequence of movements.

That allows the strategy to ask:

Is BTC moving up or down?
How quickly is it moving?
Is momentum increasing?
Is momentum fading?
Did the market reverse?
How large is the movement relative to recent prices?

This is much more useful than looking at a single price.

Coinbase as an Early Market Signal

There is an important distinction in this strategy.

Coinbase is not being used as the Polymarket settlement price.

The simplified relationship is:

Coinbase
    │
    ▼
Fast BTC Market Information
    │
    ▼
Momentum Analysis
    │
    ▼
Expected Movement

While the settlement relationship is conceptually:

BTC Market
    │
    ▼
Chainlink
    │
    ▼
60-Second TWAP
    │
    ▼
Polymarket Resolution

The trading system therefore has two different types of information:

Fast information
    +
Settlement context

The purpose of today's component is to extract the first one.

1. Connect to the Coinbase WebSocket

For a real-time system, a persistent WebSocket connection is a natural starting point.

Instead of repeatedly polling:

Bot → Request price
Bot → Request price
Bot → Request price
Bot → Request price

we want:

Coinbase
   │
   ├── Update
   ├── Update
   ├── Update
   └── Update
        ↓
       Bot

A simplified Python implementation:

import asyncio
import json
import websockets


async def price_stream():
    url = "wss://ws-feed.exchange.coinbase.com"

    async with websockets.connect(url) as ws:
        await ws.send(json.dumps({
            "type": "subscribe",
            "product_ids": ["BTC-USD"],
            "channels": ["ticker"]
        }))

        while True:
            message = await ws.recv()
            data = json.loads(message)

            print(data)


asyncio.run(price_stream())

This gives the application a continuous stream of BTC market updates.

For a production system, we would also need to consider reconnect handling, stale data detection, timestamps, validation, and monitoring.

But first, let's keep the model simple.

2. Normalize the Market Data

Raw WebSocket messages contain more information than the momentum engine needs.

We can convert each update into an internal representation:

price_update = {
    "timestamp": timestamp,
    "price": price
}

For example:

12:30:01.120 → $100,000.25
12:30:01.240 → $100,005.50
12:30:01.370 → $100,012.00
12:30:01.510 → $100,018.25

Now the rest of the application doesn't need to understand the exchange's raw message format.

It can work with a normalized market-data structure.

This separation becomes important later when adding additional data sources.

3. Maintain a Short-Term Price Window

A single price cannot tell us whether there is momentum.

We need recent history.

For example:

Time       Price
--------------------
T0         100000
T1         100005
T2         100012
T3         100018
T4         100025

From this small window, we can calculate several features:

Return
Direction
Rate of change
Momentum strength
Acceleration
Reversal

The price window becomes the input to our momentum engine.

4. Calculate Short-Term Returns

The simplest momentum feature is price return.

Suppose:

Current BTC price = $100,100
Price 1 second ago = $100,050

The absolute change is:

$100,100 - $100,050 = $50

The percentage return can be calculated as:

return_pct = (
    (current_price - previous_price)
    / previous_price
) * 100

Now the system knows more than:

BTC = $100,100

It knows:

BTC changed approximately +0.05%

That's the beginning of a useful signal.

5. Use Multiple Time Windows

One return is not enough.

A movement over one second can tell us something different from a movement over thirty seconds.

For example, we can track:

1-second return
5-second return
10-second return
30-second return

Imagine the engine observes:

1s   → +0.012%
5s   → +0.038%
10s  → +0.071%
30s  → +0.052%

This tells us much more about the current market than a single price.

We can begin to see:

Short-term direction
Movement persistence
Recent acceleration
Whether the latest movement is unusually strong

This is the type of information the decision engine will eventually consume.

6. Determine Direction

The first simple classification is:

UP
DOWN
NEUTRAL

For example:

if return_10s > threshold:
    direction = "UP"

elif return_10s < -threshold:
    direction = "DOWN"

else:
    direction = "NEUTRAL"

This gives us a basic market state.

For example:

+0.08% → UP
-0.07% → DOWN
+0.01% → NEUTRAL

However, this is not a trading decision.

It only tells us what the BTC market is doing.

7. Measure Momentum Strength

Direction alone isn't enough.

Consider two price sequences.

Weak movement

Strong movement

Both are moving upward.

But the second sequence contains significantly stronger movement.

So instead of:

Direction = UP

we want something closer to:

Direction = UP
Strength = HIGH

A normalized signal could eventually look like:

Momentum = +0.82

or:

Momentum = +0.25

The exact formula should be determined through testing rather than arbitrarily assuming that one value will work in every market regime.

8. Detect Acceleration

Momentum is also about how the movement changes over time.

Consider:

+0.01%
+0.02%
+0.04%
+0.07%

The upward movement is accelerating.

Compare it with:

+0.07%
+0.06%
+0.05%
+0.04%

The price is still moving upward, but the momentum is weakening.

These two situations should not necessarily produce the same signal.

Therefore, the momentum engine can track changes between consecutive returns.

Conceptually:

Price
  ↓
Return
  ↓
Change in Return
  ↓
Acceleration

This gives us another dimension of market state.

9. Detect Reversals

Short-term momentum can disappear quickly.

For example:

The first part shows strong upward movement.

Then the market starts moving back down.

If the strategy continues using the old signal, it can become stale.

So the momentum engine should continuously update its state.

Instead of storing only:

momentum = "UP"

we can maintain:

momentum_state = {
    "direction": direction,
    "strength": strength,
    "recent_return": recent_return,
    "previous_return": previous_return,
    "price": current_price,
    "timestamp": timestamp
}

Now the strategy can recognize transitions instead of treating momentum as a permanent state.

10. Create a Normalized Momentum State

At this point, our internal state might look like:

momentum_state = {
    "price": current_price,
    "return_1s": return_1s,
    "return_5s": return_5s,
    "return_10s": return_10s,
    "return_30s": return_30s,
    "direction": direction,
    "strength": strength,
    "timestamp": timestamp
}

This is much easier for other components to consume.

The Polymarket decision engine doesn't need to know anything about Coinbase's raw WebSocket messages.

It only needs:

Momentum State

This is a small architectural decision, but it becomes extremely useful as the system grows.

A Better System Architecture

Instead of putting everything into one function:

Receive Coinbase message
        ↓
Calculate momentum
        ↓
Read Polymarket
        ↓
Calculate probability
        ↓
Place order

we can separate responsibilities:

┌─────────────────────┐
│    Coinbase Feed    │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Market Data Layer  │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│   Momentum Engine   │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│    Signal Engine    │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│   Decision Engine   │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│     Risk Engine     │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Execution Engine   │
└─────────────────────┘

Each layer has a clear responsibility.

That makes the system easier to test, debug, and modify.

The First Version of the Signal

We can now think about the first version of our momentum signal as:

Short-Term Return
        +
Direction
        +
Strength
        +
Acceleration
        +
Reversal State
        ↓
Momentum Signal

For example:

BTC 10s Return: +0.08%
Direction:       UP
Strength:        HIGH
Momentum:        Increasing

Momentum Signal: +0.82

Or:

BTC 10s Return: -0.06%
Direction:       DOWN
Strength:        MEDIUM

Momentum Signal: -0.61

These numbers are illustrative.

The important point is the transformation:

Raw Price Updates
        ↓
Market Features
        ↓
Normalized Market State
        ↓
Momentum Signal

But We Still Cannot Trade

This is probably the most important lesson in this part of the series.

Suppose our engine produces:

Momentum Signal = +0.82

Does that mean:

BUY YES

Not necessarily.

The Coinbase signal is only one input.

The strategy also needs to understand the current Polymarket market.

For example:

Coinbase Momentum
        +
TWAP State
        +
Distance From Strike
        +
Time Remaining
        +
YES/NO Price
        +
Order-Book Depth
        +
Liquidity
        +
Spread
        +
Risk

Only after combining these variables can the decision engine determine whether the current market presents a potentially attractive setup.

The Next Problem: TWAP Context

Imagine the Coinbase engine detects:

Momentum = +0.90

Now consider two different Polymarket situations.

Scenario A

TWAP has already moved significantly
BTC is close to the strike
5 seconds remain
YES = $0.97

Scenario B

TWAP has moved very little
BTC is moving strongly
60 seconds remain
YES = $0.63

The Coinbase momentum is strong in both scenarios.

But the market context is completely different.

This is why the next layer of the system is so important.

We need to connect:

External Momentum

with:

Polymarket + TWAP State

What We Have Built

At the end of this part, the data flow looks like:

Coinbase WebSocket
        ↓
BTC Price Stream
        ↓
Short-Term Price History
        ↓
Returns
        ↓
Direction
        ↓
Momentum Strength
        ↓
Acceleration / Reversal
        ↓
Momentum Signal

We've gone from a raw stream of exchange updates to a structured signal that the rest of the trading system can consume.

But the signal is still missing its most important context.

What's Next?

In Part 3, we'll connect this momentum engine to the Polymarket market state.

We'll start working with:

YES and NO prices
BTC strike price
Current BTC price
Chainlink/TWAP-related state
Time remaining
Distance from strike
Order-book depth
Liquidity
Spread

Then we'll begin building the actual TWAP-aware decision engine.

The goal is to move from:

"BTC is moving."

to:

"BTC is moving,
the TWAP has not fully reflected the movement,
the market has enough time remaining,
the Polymarket price has not fully adjusted,
and the expected edge may justify a position."

That's the point where the system starts becoming a real market-state-driven trading architecture rather than simply a price-feed bot.

Open-Source Research Repository

I've also published a public repository containing research, examples, and educational material related to automated Polymarket trading systems.

GitHub:
https://github.com/Benjam1nCup/Polymarket-trading-bot-python-V2

The repository is intended primarily for educational and research purposes and demonstrates concepts related to automated Polymarket trading, market data, strategy development, and bot architecture.

If you're interested in Polymarket trading-bot development, strategy research, collaboration, or custom automation:

Telegram:
https://t.me/BenjaminCup

Building a TWAP-Aware Polymarket Bot with Coinbase Market Data

Benjamin-Cup — Mon, 14 Sep 2026 14:47:50 +0000

Short-duration prediction markets are extremely sensitive to timing.

A few seconds of difference between two price feeds can sometimes become an interesting signal for an automated trading system.

Before Polymarket introduced its 60-second TWAP mechanism for crypto markets, I built several trading bots around a relatively simple idea:

Use a faster external exchange price to detect movements before they were reflected on Polymarket.

You can view a real-time video of a currently active Polymarket momentum arbitrage bot at this link.

My main external data sources were Coinbase and Binance.

During fast BTC movements, I observed Coinbase moving roughly 2–3 seconds before the corresponding price movement was reflected on Polymarket.

For a short-duration market, that timing difference can be significant.

But the introduction of TWAP changed the architecture completely.

What Changed With TWAP?

Before TWAP, it was possible to think about the strategy approximately like this:

Coinbase / Binance
        ↓
   BTC spot price
        ↓
   Polymarket
        ↓
   Trade

The external exchange price could be compared relatively directly with what was happening on Polymarket.

After the TWAP mechanism was introduced, the settlement process became more time-dependent.

The simplified flow is now:

Coinbase / Binance
        ↓
   Market movement
        ↓
     Chainlink
        ↓
   60-second TWAP
        ↓
 Polymarket outcome

This creates an important distinction.

If BTC suddenly moves on Coinbase, the Polymarket settlement price does not necessarily move to the same price immediately.

The TWAP incorporates price information over a time window.

So a bot needs to understand both:

the current market price
how the TWAP is likely to evolve

Coinbase Is Still Useful

The TWAP upgrade did not make Coinbase data irrelevant.

It changed how I use it.

I no longer treat Coinbase as a replacement for the settlement price.

Instead, I use Coinbase as an early market signal.

During fast market movements, Coinbase can still move before the corresponding Chainlink/TWAP-related value fully reflects the movement.

In my observations, this difference can be around 2–3 seconds.

That gives the strategy a different objective.

Instead of:

Coinbase price
      ≠
Polymarket price

→ Arbitrage

the idea becomes:

Coinbase momentum
        ↓
Expected Chainlink movement
        ↓
Expected TWAP movement
        ↓
Polymarket market state
        ↓
Trade decision

This is the foundation of my Momentum Arbitrage Bot.

From Price Arbitrage to Momentum Arbitrage

The original strategy was closer to:

Coinbase moved
      ↓
Polymarket hasn't reacted
      ↓
Trade the difference

The newer strategy is more like:

Coinbase is moving
      ↓
Chainlink should follow
      ↓
TWAP will gradually incorporate the movement
      ↓
Analyze the expected effect
      ↓
Evaluate Polymarket pricing
      ↓
Trade if the conditions are favorable

This is not simply arbitraging two prices.

The bot is looking for a timing and information gap between several systems:

Coinbase market data
Chainlink price updates
60-second TWAP
Polymarket order book
market strike
time remaining until resolution

That last variable is especially important.

The same BTC movement can have completely different implications depending on whether there are:

10 minutes remaining
        ↓
5 minutes remaining
        ↓
60 seconds remaining
        ↓
10 seconds remaining
        ↓
Resolution

What Does the Bot Monitor?

A useful implementation needs more than a Coinbase WebSocket.

The trading engine should maintain several pieces of state.

1. Coinbase Price Feed

Coinbase provides the fast external market signal.

The bot can track:

BTC/USD
Current price
Price change
Short-term momentum
Update frequency

The purpose is to detect sudden market movements as early as possible.

2. Chainlink / TWAP State

The bot also needs settlement-related information.

For example:

Chainlink price
TWAP value
TWAP direction
TWAP change
Distance from strike

Coinbase provides the early signal.

Chainlink/TWAP provides the settlement context.

The two should not be treated as interchangeable.

3. Polymarket Order Book

A directional signal by itself is not enough.

The bot also needs to understand the actual trading environment.

For example:

YES price
NO price
Bid depth
Ask depth
Spread
Liquidity
Order-book imbalance
Available size

A strong momentum signal with poor liquidity may not be worth trading.

Execution quality matters just as much as signal quality.

4. Time Remaining

Time is one of the most important variables in a short-duration market.

For example:

Market starts
     ↓
10 minutes
     ↓
5 minutes
     ↓
1 minute
     ↓
10 seconds
     ↓
Resolution

A BTC movement early in the market may not have the same effect as the same movement close to resolution.

Therefore, the strategy needs to be time-aware.

A Simple Example

Imagine a BTC Up/Down market.

Suppose Coinbase shows:

BTC = $100,000

Then BTC suddenly moves:

$100,000 → $100,150

Coinbase can reflect this movement almost immediately.

The Chainlink/TWAP-related value does not necessarily jump to $100,150 at the same time.

Instead, the new information gradually affects the 60-second TWAP.

Conceptually:

T0
Coinbase moves
      ↓
T1
Chainlink begins reflecting the movement
      ↓
T2
TWAP incorporates the new price information
      ↓
T3
Polymarket settlement-related state changes

The trading engine is therefore trying to answer:

Can the Coinbase movement provide enough information about the future TWAP movement to justify a position before the market fully incorporates it?

That is the core question behind the strategy.

The Signal

A simplified version of the decision model looks like this:

Fast Market Movement
        +
TWAP Lag
        +
Time Remaining
        +
Distance From Strike
        +
Polymarket Liquidity
        ↓
   Trade Decision

The bot should not simply buy whenever BTC moves.

Instead, it needs to evaluate questions such as:

How large is the Coinbase movement?
How quickly did the movement happen?
Is momentum continuing?
Is the market reversing?
What is the current TWAP?
Where is BTC relative to the strike?
How much time remains?
How much of the movement has already reached the TWAP?
What are the current YES/NO prices?
Is there enough liquidity for execution?
Does the expected edge justify fees, slippage, and risk?

This transforms a simple price-feed bot into a market-state-aware trading system.

System Architecture

A simplified architecture looks like this:

                  ┌───────────────┐
                  │    Coinbase   │
                  │  Price Feed   │
                  └───────┬───────┘
                          │
                          ▼
                  ┌───────────────┐
                  │   Momentum    │
                  │    Engine     │
                  └───────┬───────┘
                          │
                          ▼
┌──────────────┐   ┌───────────────┐   ┌──────────────┐
│  Chainlink   │──▶│ Signal Engine │◀──│  Polymarket  │
│   / TWAP     │   │               │   │ Order Book   │
└──────────────┘   └───────┬───────┘   └──────────────┘
                           │
                           ▼
                    ┌────────────┐
                    │ Risk Engine│
                    └─────┬──────┘
                          │
                          ▼
                    ┌────────────┐
                    │ Execution  │
                    │   Engine   │
                    └────────────┘

Each component has a specific responsibility.

Coinbase

Provides the fast external market signal.

Momentum Engine

Processes price updates and detects short-term momentum.

Chainlink / TWAP

Provides settlement-related context.

Polymarket

Provides the prediction-market state and order book.

Risk Engine

Determines whether a potential trade satisfies the strategy's risk constraints.

Execution Engine

Handles order submission, monitoring, and execution.

What We'll Build

In the next parts of this tutorial, I'll go deeper into the implementation.

The project will cover:

Connecting to Coinbase's real-time price feed
Processing BTC price updates
Calculating short-term momentum
Tracking Chainlink/TWAP-related state
Reading Polymarket market data
Comparing external momentum with Polymarket pricing
Building the momentum-arbitrage signal
Adding risk controls
Designing the execution layer
Testing the strategy with live and historical data

The goal is not to create a guaranteed-profitable bot.

Trading strategies can stop working as market conditions change.

Latency, liquidity, fees, slippage, execution quality, TWAP behavior, and market structure all matter.

The goal is to understand how to build the system and how the different data sources interact.

Source Code

I've published a public repository with examples and educational material related to my Polymarket trading-bot development:

Benjam1nCup / Polymarket-trading-bot-python-V2

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…

View on GitHub

The repository is intended for educational purposes.

For questions, collaboration, or custom trading-bot development:

Telegram: https://t.me/BenjaminCup

What's Next?

In the next part, we'll start with the most important component:

Building a real-time Coinbase price feed and converting raw BTC price updates into a short-term momentum signal.

From there, we'll connect that signal to the Polymarket market state and start building the actual decision engine.

How to Build a Pons Bundler Bot on Robinhood Chain

Benjamin-Cup — Sun, 13 Sep 2026 15:59:00 +0000

Token launches on Robinhood Chain can become interesting when launch mechanics include a temporary snipe tax.

In this tutorial, I'll show the basic architecture behind a Pons V2 Bundler Bot and how to build a simple one-wallet launchAndBuy implementation.

This is an educational/research project designed to help developers understand how automated on-chain trading systems work.

What We're Building

The basic workflow is:

Pons Factory
     ↓
Check canLaunch()
     ↓
Read launch configuration
     ↓
Read current snipe tax
     ↓
Build launchAndBuy()
     ↓
Simulate transaction
     ↓
Sign & execute

The bot can provide commands such as:

pnpm pons index

pnpm pons can-launch 0xYourWallet

pnpm pons tax \
  --token 0xToken \
  --wallet 0xWallet

pnpm pons preview \
  --config examples/launch.json

pnpm pons launch \
  --config examples/launch.json

Understanding the Snipe Tax

One of the interesting parts of Pons V2 is the temporary launch tax.

The contract exposes:

currentSnipeTaxBps

where the value is represented in basis points.

For example:

100 bps   = 1%
1000 bps  = 10%
9900 bps  = 99%

The tax decreases as the launch progresses.

This means that buying immediately after launch can be very different from buying later.

The interesting part is the relationship between the launch transaction and its recipient.

With launchAndBuy, the launch and initial purchase can happen together, allowing us to study how the launch contract treats the initial recipient compared with another wallet.

For example:

LaunchAndBuy recipient
        ↓
      0 bps

Random wallet
        ↓
   ~9900 bps

The exact behavior should always be verified against the current Pons V2 contracts.

Building the Bot

The project can be organized into a few simple modules:

src/
├── cli.ts
├── launch.ts
└── tax.ts

examples/
└── launch.json

`cli.ts`

Handles commands such as:

pons index
pons can-launch
pons tax
pons preview
pons launch

`launch.ts`

Handles:

Factory configuration
canLaunch
launchAndBuy
Transaction construction
Transaction simulation

`tax.ts`

Reads:

currentSnipeTaxBps

for a specific token and wallet.

Transaction Safety

One important detail is msg.value.

The launch transaction may require the launch fee plus the initial buy amount:

msg.value =
    launch fee
  + first buy

Before sending the transaction, the bot should:

Validate the configuration.
Check canLaunch.
Calculate the required value.
Simulate the transaction.
Only then sign and broadcast.

I also recommend making execution explicit:

pnpm pons launch --config examples/launch.json

for a dry run, and:

pnpm pons launch \
  --config examples/launch.json \
  --live

when you intentionally want to send the transaction.

Security

Never put private keys directly into source code.

Use a local .env:

PONSBOT_PRIVATE_KEY=0x...
RPC_URL=https://rpc.mainnet.chain.robinhood.com

and add .env to .gitignore.

For testing, use a dedicated wallet with only the funds required for the experiment.

What's Next?

The one-wallet implementation is only the starting point.

A larger system could add:

Multi-wallet coordination
Real-time launch monitoring
Transaction tracking
Telegram notifications
Risk filters
Wallet management
Execution monitoring

But I recommend understanding the single-wallet flow first. Once you understand the contract calls, tax calculation, transaction value, and execution flow, the larger architecture becomes much easier to build.

Source Code

I've collected my Robinhood Chain trading-bot research, strategies, and development projects here:

[Robinhood Trading Bot System — GitHub]

Benjam1nCup / Robinhood-Trading-Bot-System

Robinhood Token Trading Bot Robinhood Bot Robinhood copy trading bot Robinhood sniper bot Robinhood bundler bot Robinhood volume booster bot Robinhood Chain Trading Bot Robinhood Bot Robinhood copy trading bot Robinhood sniper bot Robinhood bundler bot Robinhood volume booster bot Robinhood Chain Trading Bot Robinhood Bot Robinhood copy trading bot

Robinhood Chain Trading Bot | Robinhood Chain Sniper Bot | Robinhood Chain Copy Trading Bot

An open-source and Strong Strategy collection of Robinhood Chain trading bot and Robinhood Chain sniper bot and Robinhood Chain copy trading bot in Python for high-performance automated on-chain trading.

Robinhood Chain is an Ethereum-compatible Layer-2 blockchain built with Arbitrum technology. The mainnet uses Chain ID 4663, ETH as the native gas token, and provides EVM-compatible infrastructure for developers building on-chain applications and trading systems.

If you…

View on GitHub

The repository covers different Robinhood Chain bot concepts, including sniper bots, copy trading, token launch research, liquidity strategies, arbitrage, and other automated trading ideas.

If you're interested in custom Robinhood Chain trading-bot development or want to discuss this project:

Telegram: [@BenjaminCup]
https://t.me/BenjaminCup

Disclaimer: This project is for educational and research purposes. Always verify smart-contract addresses and current protocol mechanics before interacting with a live blockchain. Never expose your private keys.