Polymarket Bot Backtesting: I Thought My Strategy Worked Until I Simulated Execution

Why realistic fills, latency, slippage, liquidity, and execution matter more than a perfect backtest.

Casatrick

Aug 21, 2026

I thought my Polymarket bot strategy worked.

The backtest looked profitable.

The win rate looked good.

The signals were consistent.

So the obvious next step was to improve execution and put the strategy into live testing.

Then I started looking more carefully at what the backtest was actually assuming.

That changed my opinion completely.

The problem wasn’t necessarily the strategy.

The problem was that my backtest was making the market easier to trade than it actually is.

Perfect fills.

No meaningful latency.

Unlimited liquidity.

No partial fills.

No stale order books.

No competition for the same liquidity.

No execution failures.

In other words, I wasn’t really backtesting my Polymarket bot.

I was backtesting a simplified version of the market.

And those are two very different things.

The Problem With Most Polymarket Bot Backtests

A simple trading bot backtest usually looks like this:

Historical Price
      ↓
Generate Signal
      ↓
     Buy
      ↓
     Sell
      ↓
Calculate P&L

It looks reasonable.

But there’s a major assumption hidden inside the process:

If my strategy generates a signal at a particular price, I can trade at that price.

Real markets don’t work that way.

A more realistic process looks like:

Historical Market Data
        ↓
Signal Generated
        ↓
Detection Delay
        ↓
Order Submission
        ↓
Current Order Book
        ↓
Available Liquidity
        ↓
Partial / Full Fill
        ↓
    Slippage
        ↓
      Fees
        ↓
Position Management
        ↓
  Realized P&L

Every step can change the result.

And when you’re building a Polymarket trading bot around short-lived opportunities, small differences can completely change whether a strategy is profitable.

What I Used to Assume

The simplest version of my thinking was:

Market price = 70¢

My model says fair value = 76¢

Edge = 6¢

BUY

Then the backtest records something like:

Entry:  $0.70
Exit:   $0.76
Profit: $0.06

Do that thousands of times and the strategy can look very attractive.

But what if the order book actually looked like this?

$0.70 → 10 contracts
$0.71 → 20 contracts
$0.72 → 50 contracts
$0.73 → 100 contracts
$0.74 → 200 contracts

If the bot wants to buy 500 contracts, it isn’t buying everything at $0.70.

The actual average execution price might be significantly higher.

That difference is slippage.

And the backtest needs to know about it.

Problem #1: Perfect Fills

This is probably the biggest problem.

Suppose your strategy generates:

BUY YES @ $0.70
Size: 1,000

A naive backtest records:

1,000 × $0.70

But maybe only 50 contracts were actually available at $0.70.

The rest might have been available at:

$0.71
$0.72
$0.73
$0.74

Now your real average price could be closer to:

$0.73

instead of:

$0.70

That changes the economics of the trade.

A 3¢ difference might not sound significant.

Multiply it across thousands of trades and suddenly your backtested edge disappears.

Problem #2: The Best Bid/Ask Isn’t Your Execution Price

A market might display:

Best ask: $0.70

That doesn’t mean:

I can buy any amount I want for $0.70.

It means there is some liquidity available at that price.

The important question is:

How much?

A trading bot should therefore think about fillable quantity, not just displayed price.

For example:

Price       Available

$0.70       50
$0.71       100
$0.72       150
$0.73       200

If your order size is 500:

50 @ $0.70
100 @ $0.71
150 @ $0.72
200 @ $0.73

Your effective entry price is completely different from the best ask.

A realistic Polymarket bot backtest needs to simulate this.

Problem #3: Slippage

Slippage is the difference between the price your strategy expects and the price at which the order actually executes.

For a buy:

Expected: $0.70
Actual:   $0.73

Slippage: $0.03

For a strategy with a theoretical edge of $0.04, paying $0.03 in slippage leaves almost nothing.

This is why I prefer to think in terms of:

Gross Edge-Execution Costs=Net Edge

rather than:

Model Price-Market Price=Profit

The second calculation is too optimistic.

Problem #4: Latency

This became particularly obvious when I switched my own Polymarket bot from polling to WebSocket market data.

My old system was polling prices every 30 seconds.

That meant the average detection delay was about 14.8 seconds.

After switching to a WebSocket feed and a 500ms scan interval, average detection lag dropped to approximately 0.31 seconds.

That’s roughly a 48× improvement.

But something unexpected happened.

My win rate went from:

73%

to:

71%

The faster system detected far more signals.

But not all of those signals were good signals.

This was an important lesson:

A backtest that ignores latency can make a strategy look much better than it really is.

And the opposite can also happen.

A strategy designed around slow data can behave differently when given real-time data.

Speed changes the distribution of signals.

So if your production bot uses WebSockets, your backtest should ideally simulate the same information timing.

Problem #5: Stale Data

Imagine this sequence:

12:00:00.000
Market = $0.70

12:00:00.200
Market = $0.73

12:00:00.400
Market = $0.76

Your backtest sees the price at 12:00:00.000 and generates a signal.

But if your simulated execution happens at 12:00:00.500, the $0.70 opportunity may already be gone.

A backtest that simply says:

signal price = execution price

is assuming zero latency.

That’s rarely realistic.

A better simulation is:

Market Event
     ↓
Signal Calculation
     ↓
Detection Latency
     ↓
Network / Processing Latency
     ↓
Order Submission
     ↓
Historical Order Book
     ↓
Fill Simulation

This is much closer to what a real Polymarket bot experiences.

Problem #6: Partial Fills

Partial fills are especially important for arbitrage bots.

Suppose your bot sees:

YES + NO < $1

and decides to buy both sides.

The theoretical arbitrage looks like:

YES = $0.47
NO  = $0.51

Total = $0.98

Looks great.

But imagine the order book only provides enough liquidity for the YES side.

Your bot gets:

YES → FILLED
NO  → PARTIALLY FILLED

Now you’re no longer holding the risk-free combination you expected.

You’re exposed to one side.

This is exactly why I previously argued that the difficult part of Polymarket arbitrage isn’t necessarily finding the edge.

It’s keeping the edge long enough to execute it.

A backtest that assumes both legs always fill perfectly is massively optimistic.

Problem #7: Arbitrage Is Especially Difficult to Backtest

Arbitrage looks mathematically simple.

For example:

YES + NO < $1.00

Potential opportunity.

But real execution is more complicated.

Imagine:

YES = $0.47
NO  = $0.51

Combined = $0.98

Your theoretical edge is:

$0.02

Then consider:

Spread
+
Slippage
+
Latency
+
Partial fills
+
Fees
+
Competition

Your $0.02 edge can disappear very quickly.

This is why I don’t trust an arbitrage backtest that only uses midpoint prices.

The backtest needs to understand actual executable liquidity.

Problem #8: Midpoint Prices Can Lie

Suppose the market shows:

Bid = $0.70
Ask = $0.72

The midpoint is:

$0.71

A backtest might use $0.71 as the market price.

But if your strategy is buying, you don’t get $0.71.

You’re interacting with the ask.

If your strategy is selling, you’re interacting with the bid.

So instead of:

midpoint

you should often simulate:

buy → ask-side execution

sell → bid-side execution

and preferably the complete depth of the book when order size is meaningful.

The Backtest I Trust More

My current mental model for a serious Polymarket bot backtest looks like this:

Historical Order Book
        ↓
Timestamped Events
        ↓
     Strategy
        ↓
      Signal
        ↓
Latency Model
        ↓
Execution Simulator
        ↓
Historical Depth
        ↓
Partial Fill Model
        ↓
Slippage Model
        ↓
       Fees
        ↓
Position Manager
        ↓
       P&L

That is much more work.

But it answers a much more useful question:

“Would this bot have been able to make this trade?”

instead of:

“Would this strategy have predicted the right direction?”

Those are not the same question.

A Better Polymarket Bot Backtesting Architecture

If I were designing a backtesting system from scratch, I’d separate it into several components.

backtest/
│
├── data/
│   ├── markets
│   ├── orderbooks
│   └── trades
│
├── strategy/
│   ├── signals
│   └── probability
│
├── execution/
│   ├── latency
│   ├── slippage
│   ├── fills
│   └── cancellation
│
├── portfolio/
│   ├── positions
│   ├── exposure
│   └── pnl
│
└── metrics/
    ├── performance
    ├── drawdown
    └── execution

The important design decision is that the strategy should not directly calculate P&L.

Instead:

Strategy
   ↓
 Signal
   ↓
Execution Simulator
   ↓
  Fill
   ↓
Position
   ↓
  P&L

This makes the system much easier to reason about.

Step 1: Store Historical Market Data

You need more than a sequence like:

12:00 → $0.70
12:01 → $0.71
12:02 → $0.69

For execution-sensitive strategies, you ideally want timestamped order-book information.

Something closer to:

timestamp
asset_id
bid_price
bid_size
ask_price
ask_size
depth
trade events

The more accurately you can reconstruct the state of the market, the more realistic the backtest becomes.

Step 2: Replay the Market

Instead of loading the entire dataset and calculating signals from the final state, replay events chronologically.

Conceptually:

for event in historical_events:
    update_order_book(event)

    signal = strategy.evaluate(order_book)

    if signal:
        execution_engine.process(signal, order_book)

This matters because your strategy should only know what was available at that point in time.

It shouldn’t have access to future information.

Step 3: Add Detection Latency

Suppose:

Market event:
12:00:00.000

Signal generated:
12:00:00.050

Order submitted:
12:00:00.150

The execution simulator should use the order book around the time the order would actually arrive.

Not the order book at:

12:00:00.000

That small detail can have a huge impact on short-duration markets.

Step 4: Simulate Available Depth

Suppose your order is:

BUY 1,000

and the book is:

$0.70 → 100
$0.71 → 200
$0.72 → 300
$0.73 → 400

The simulator should consume the book:

100 @ $0.70
200 @ $0.71
300 @ $0.72
400 @ $0.73

Then calculate:

VWAP

for the entire order.

Now the backtest knows what your actual average execution price would have been.

Step 5: Simulate Partial Fills

If there isn’t enough liquidity:

Requested = 1,000

Available = 650

you have choices.

The simulator could model:

650 filled
350 unfilled

Then the strategy needs to decide what happens next.

Does it:

Cancel?
Retry?
Wait?
Cross the spread?
Reduce position?

This is strategy-specific.

But the backtest should not silently pretend the remaining 350 were filled.

Step 6: Simulate Order Cancellation

Real bots cancel orders.

For example:

Signal generated
       ↓
Order submitted
       ↓
Market moves
       ↓
Expected edge disappears
       ↓
  Cancel order

A backtest should account for this behavior.

Otherwise, resting orders can appear to receive fills that would never realistically happen.

Step 7: Calculate Realistic P&L

Now you can calculate:

Gross P&L - Fees - Slippage - Execution Costs= Net P&L

Then evaluate:

Net return
Maximum drawdown
Profit factor
Expected value
Sharpe-like metrics
Win rate
Average trade
Average loss
Average win

The exact metrics depend on the strategy.

But net P&L after realistic execution is much more important than a theoretical gross return.

Why Win Rate Is Not Enough

This is another trap.

Imagine two bots.

Bot A

Win rate: 90%

Average win: +$0.01
Average loss: -$0.15

Bot B

Win rate: 60%

Average win: +$0.15
Average loss: -$0.05

Bot A looks better if you’re only looking at win rate.

But Bot B may have a much better expected value.

A simplified expected value calculation is:

EV =(win probability × average win)-(loss probability × average loss)

For Bot A:

0.90 × $0.01-0.10 × $0.15 = -$0.006

For Bot B:

0.60 × $0.15-0.40 × $0.05 = +$0.07

The exact numbers are illustrative.

The point is:

Win rate alone tells you almost nothing about whether a trading bot is profitable.

The Metrics I Watch

For a Polymarket trading bot, I’d monitor at least:

Total trades
Win rate
Average win
Average loss
Expected value
Profit factor
Maximum drawdown
Total volume
Average position size
Average holding time
Fees
Slippage
Partial-fill rate
Order rejection rate
Signal-to-order latency
Order-to-fill latency

And for arbitrage:

Detected opportunities
Executable opportunities
Orders submitted
Both legs filled
One-leg exposure
Average opportunity lifetime
Average available depth
Average captured edge

That distinction between:

Detected

and:

Executable

is extremely important.

Detected Opportunities vs Executable Opportunities

Imagine your bot detects:

1,000 arbitrage opportunities

That sounds impressive.

But after filtering:

1,000 detected
   ↓
600 pass liquidity
   ↓
350 pass size requirements
   ↓
180 survive latency
   ↓
90 survive slippage
   ↓
40 actually execute

Now you understand the real strategy.

The original number wasn’t necessarily useful.

The executable number is.

This is one of the biggest differences between a research script and a production Polymarket bot.

Backtesting and My WebSocket Experiment

This is where my recent WebSocket experiment became particularly interesting.

After moving from 30-second polling to WebSockets:

Detection lag

14.8s → 0.31s

Signals detected per day also increased:

147 → 312

And false positives dropped:

38% → 11%

But win rate moved from:

73% → 71%

The faster feed exposed a problem in my signal logic.

My original thresholds had effectively been calibrated against stale data.

When the data became much fresher, the bot started seeing short-lived movements that the old system simply missed.

That is exactly why backtesting and live testing need to be treated as separate but connected systems.

Changing the data resolution can change the strategy itself.

A Backtest Can Be Correct and Still Be Wrong

This sounds contradictory.

But it’s not.

Your code can be perfectly correct.

Your calculations can be perfectly correct.

Your historical dataset can be accurate.

And the result can still be misleading because the simulation doesn’t represent the execution environment.

For example:

Historical price: accurate
Strategy: correct
Signal: correct
Fill: unrealistic

The backtest is technically correct.

But it isn’t answering the question you actually care about.

The question isn’t:

“Did my strategy identify a favorable price?”

The question is:

“Could my Polymarket bot have actually captured that favorable price?”

Out-of-Sample Testing

Another important problem is overfitting.

Imagine you test:

Entry threshold = 0.01
Entry threshold = 0.02
Entry threshold = 0.03
...
Entry threshold = 0.20

Eventually you’ll find a parameter combination that looks amazing.

But that doesn’t mean you’ve found a robust strategy.

You may simply have optimized for historical noise.

A better approach is:

Historical Data
      ↓
Training Period
      ↓
Parameter Selection
      ↓
Validation Period
      ↓
Final Out-of-Sample Test

The strategy should be evaluated on data it wasn’t optimized against.

Otherwise you’re measuring how well it remembers the past.

Not how well it generalizes.

Walk-Forward Testing

For a strategy that changes over time, walk-forward testing can be even more useful.

Conceptually:

Train → Test
      ↓
Move Window
      ↓
Train → Test
      ↓
Move Window
      ↓
Train → Test

This better reflects the reality that Polymarket markets evolve.

A parameter that worked three months ago may not work today.

Liquidity changes.

Competition changes.

Market participants change.

The opportunity itself can disappear.

What About AI or Machine Learning?

Machine learning can make Polymarket bot research more interesting.

But it doesn’t solve the execution problem.

A model can predict:

Probability = 76%

while the market is:

Price = 70¢

That looks like a 6-point edge.

But if your actual executable price is:

74¢

and the market moves before your order arrives, the practical edge is much smaller.

The model doesn’t know how much liquidity is available unless you explicitly give it that information.

So I’d separate:

Prediction Model

from:

Execution Model

Both matter.

The Biggest Lesson

After working through these systems, I’ve become much less interested in asking:

“Does the backtest make money?”

Now I ask:

“What assumptions are required for the backtest to make money?”

That’s a much better question.

If the strategy only works with:

Perfect fills
Zero latency
Unlimited liquidity
No slippage
No fees
No partial fills

then it doesn’t really work.

If the strategy survives:

Realistic latency
Realistic depth
Realistic fills
Realistic slippage
Fees
Partial fills
Order cancellations

then I’m much more interested.

My Current Polymarket Bot Testing Process

My preferred workflow is now:

1. Strategy idea
       ↓
2. Historical data
       ↓
3. Basic backtest
       ↓
4. Realistic execution simulation
       ↓
5. Out-of-sample testing
       ↓
6. Paper trading
       ↓
7. Small live deployment
       ↓
8. Compare live vs backtest
       ↓
9. Recalibrate
       ↓
10. Scale carefully

The most important comparison is:

Backtest
   vs
Paper Trading
   vs
Live Trading

If those three numbers are wildly different, something is wrong.

And that’s useful information.

What I Would Never Trust in a Polymarket Bot Backtest

If I see a backtest with:

99% win rate

my first question isn’t:

“What strategy is this?”

It’s:

“How are fills simulated?”

If the answer is:

We use historical midpoint prices.

I’m immediately skeptical.

If the answer is:

We replay historical order-book events, model latency, consume available depth, simulate partial fills, account for slippage and fees, and validate out-of-sample.

Now I want to see the results.

The difference isn’t cosmetic.

It’s the difference between a strategy simulation and an execution simulation.

The Future of Polymarket Bots

As more automated traders enter prediction markets, I think the competitive advantage will increasingly move away from simple signal detection.

A simple strategy can be copied.

A simple arbitrage calculation can be copied.

A simple price scanner can be copied.

But a complete system is much harder to reproduce.

The competitive stack increasingly looks like:

Better Data
    +
Better Signal
    +
Better Execution
    +
Better Risk Management
    +
Better Infrastructure
    +
Better Research

That’s where I think Polymarket bot development becomes genuinely interesting.

Final Thoughts

I started this process thinking backtesting was mainly about answering one question:

“Would my strategy have made money?”

Now I think that’s only the beginning.

The better questions are:

“Could the trade actually have been executed?”
“How much liquidity was really available?”
“How much latency did the strategy have?”
“How much of the theoretical edge survived execution?”
“What happened when the order only partially filled?”
“Does the strategy still work on data it wasn’t optimized against?”

Those questions produce much more useful answers.

A Polymarket bot isn’t profitable because its backtest has a beautiful equity curve.

It’s profitable only if the edge survives the journey from:

Market Data
    ↓
 Signal
    ↓
 Latency
    ↓
  Order
    ↓
Liquidity
    ↓
  Fill
    ↓
Position
    ↓
Settlement

That’s the part I care about now.

The goal isn’t to build the backtest with the highest return.

The goal is to build a backtest that is difficult to fool.

And if a strategy still looks good after you’ve made the simulation hostile to your assumptions, that’s when it starts getting interesting.

What I’m Testing Next

I’m currently more interested in execution-aware testing than simply finding another signal.

The next things I’d want to measure are:

historical order-book replay
signal-to-order latency
order-to-fill latency
partial-fill probability
liquidity decay after signal detection
slippage by order size
opportunity lifetime
executable vs theoretical arbitrage
live-vs-backtest divergence

Because ultimately:

The best Polymarket bot isn’t the one with the best-looking backtest.

It’s the one whose backtest survives contact with the real market.

Disclaimer: This article reflects my personal experience researching and testing automated trading systems on Polymarket. It is for educational and technical purposes only and is not financial advice. Backtested or simulated results do not guarantee future performance.

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