r/PredictionMarkets

Source: https://x.com/BurwickLaw/status/2074200534102237288

If you buy NO on Kharg Island at 98 cents, the market will hand you 2.04% when it settles on August 31. That's roughly 13% annualized.

The market is asking whether Kharg Island stops being Iranian by the end of August.

Kharg is a coral speck 20 miles off the coast that handles roughly 90% of Iran's crude exports. Betting on NO means betting nothing happens.

This is one of the most important islands in the world, and with the Iran-US-Israel conflict quieter now, it's unlikely that anything of that caliber will happen in the next 60 days.

Originally posted on Predictbook: https://x.com/Predictbook/status/2073861323251658809

🎙️ We're looking for industry experts to join our upcoming live interview series!

We're launching a new 30-minute daily livestream focused on prediction markets and the events shaping them.

We're looking to speak with people who have experience or insights in areas such as:

* 📈 Prediction markets (Polymarket & similar platforms)

* ⚽ Sports

* 🗳️ Politics & elections

* 💰 Crypto & Web3

* 📊 Economics & finance

* 🤖 AI & technology

* 🎬 Entertainment predictions (Emmys, Oscars, etc.)

* ...and other timely topics.

If you actively follow these markets, trade on them, build in the space, conduct research, or analyze trends, we'd love to connect.

The interviews are casual, live conversations (around 30 minutes) where you can share your insights with our growing audience.

Interested, or know someone who might be a good fit? Feel free to comment below or send me a direct message.

Looking forward to connecting with more experts in the space!

#PredictionMarkets #Polymarket #Crypto #SportsBetting #Forecasting #Economics #Politics #AI #Web3 #Finance #Podcast #Livestream #HiringExperts

Most market forecasters have the same problem.

They post a forecast in the morning. The market closes. They move on.

Nobody measures. Nobody improves.

I decided to change that.

The Problem With "I Was Right"

After years of analyzing Nifty 50 intraday movements, I realized something uncomfortable.

I could look at my forecast at 3:30 PM and say "I got the direction right." But that told me almost nothing useful.

Was I right at 9:15 AM or only after 2:00 PM? Was my model 10 minutes early or 10 minutes late? Did I get the morning session right but miss the afternoon? Was Model A better than Model B today — and by how much?

These questions had no answers. Until I built something to answer them automatically.

What I Built

A real-time Nifty 50 forecast accuracy engine that runs , updates every minute during market hours, and computes 30 different metrics automatically.

It looks like a standard chart. But under the hood it is doing something most trading tools don't do — comparing forecast shape against live market data, minute by minute, all day long.

Here is what it tracks:

Correlation metrics:

• Full day Pearson correlation
• Last 60, 30, 15 and 5 minute rolling windows
• Best matching 30-minute window of the day
• Worst matching 30-minute window of the day

Direction accuracy:

• Overall up/down direction match percentage
• Up move accuracy separately
• Down move accuracy separately
• Longest correct direction streak
• Current streak at any moment

Magnitude accuracy:

• Average error per bar in points
• Percentage of bars within 5, 10 and 20 points
• Maximum error (worst single minute)

Time shift detection:

• Is the forecast running early or late vs actual?
• By how many minutes?
• At what shift does correlation peak?

Session analysis:

• Morning session match (9:15 to 12:00)
• Afternoon session match (12:00 to 15:30)

Trend accuracy:

• Did forecast predict the right day direction?
• Did it catch the peak within 30 minutes?
• Did it catch the trough within 30 minutes?
• How close was the forecast high vs actual high?
• How close was the forecast low vs actual low?
• End of day accuracy

Overall:

• Composite weighted score
• Automatic ranking when running multiple models

The Discovery That Changed Everything

The most surprising metric was time shift.

For weeks my correlation scores looked decent — around 65 to 70 percent. I thought that was reasonable. Then I added time shift detection.

It showed my model was consistently running 10 to 15 minutes ahead of the actual market.

The forecast shape was correct. The timing was off.

Once I knew that, I could account for it. Within two weeks my full day correlation jumped from 68 percent to 81 percent — not because my model got better, but because I finally understood how it was wrong.

You cannot fix what you cannot measure.

Running Multiple Models

The second insight came from comparing models side by side.

I run three different forecast approaches each morning. Before this tool I would look at them visually and pick the one that "felt" most reasonable.

Now I have a comparison table. Every metric. Every model. Automatically ranked.

Some days Model A wins on correlation but Model B wins on direction accuracy. Some days one model nails the morning session while another gets the afternoon right.

The table shows exactly where each model is strong and where it falls apart. That is information you cannot get from looking at lines on a chart.

The chart itself has full interactions — hover tooltips, crosshair, zoom, pan, timeframe switching from 1 minute to 30 minutes, moving averages. What the Hover Shows

When you move your cursor over the chart you see:

• Exact time label
• Live Nifty value at that minute (change from open)
• Each forecast model value at that minute
• Difference between actual and forecast in points

In the analysis table every cell highlights the best performer in green. You can see at a glance which model is winning, which metric each model leads, and what the composite score is right now.

What This Is Not

This is not a trading system. It does not give buy or sell signals.

It is a measurement and improvement tool. Its job is to tell me honestly how accurate my forecast was today — in 30 different ways — so I can understand my model better and improve it over time.

The goal is not to be right every day. The goal is to understand exactly how and when and why I am wrong, so the model gets better over time.

What Is Next

will update and have real time from Monday or whatever possible at earliest

The Bigger Point

Anyone can post a forecast. Very few people measure it rigorously.

If you are serious about market forecasting — intraday or otherwise — you need a measurement system as rigorous as your forecasting system.

Otherwise you are flying blind and calling it analysis.

Build the feedback loop. Measure everything. Improve systematically.

That is how forecasting becomes a skill rather than a guess.

*I publish daily Nifty 50 intraday forecasts along with real-time accuracy tracking. Follow for updates on methodology, results and the ongoing development of this tool.*They post a forecast in the morning. The market closes. They move on.

Nobody measures. Nobody improves.

I decided to change that.

The Problem With "I Was Right"

After years of analyzing Nifty 50 intraday movements, I realized something uncomfortable.

I could look at my forecast at 3:30 PM and say "I got the direction right." But that told me almost nothing useful.

Was I right at 9:15 AM or only after 2:00 PM? Was my model 10 minutes early or 10 minutes late? Did I get the morning session right but miss the afternoon? Was Model A better than Model B today — and by how much?

These questions had no answers. Until I built something to answer them automatically.

What I Built

A real-time Nifty 50 forecast accuracy engine that runs , updates every minute during market hours, and computes 30 different metrics automatically.

It looks like a standard chart. But under the hood it is doing something most trading tools don't do — comparing forecast shape against live market data, minute by minute, all day long.

Here is what it tracks:

Correlation metrics:

• Full day Pearson correlation
• Last 60, 30, 15 and 5 minute rolling windows
• Best matching 30-minute window of the day
• Worst matching 30-minute window of the day

Direction accuracy:

• Overall up/down direction match percentage
• Up move accuracy separately
• Down move accuracy separately
• Longest correct direction streak
• Current streak at any moment

Magnitude accuracy:

• Average error per bar in points
• Percentage of bars within 5, 10 and 20 points
• Maximum error (worst single minute)

Time shift detection:

• Is the forecast running early or late vs actual?
• By how many minutes?
• At what shift does correlation peak?

Session analysis:

• Morning session match (9:15 to 12:00)
• Afternoon session match (12:00 to 15:30)

Trend accuracy:

• Did forecast predict the right day direction?
• Did it catch the peak within 30 minutes?
• Did it catch the trough within 30 minutes?
• How close was the forecast high vs actual high?
• How close was the forecast low vs actual low?
• End of day accuracy

Overall:

• Composite weighted score
• Automatic ranking when running multiple models

The Discovery That Changed Everything

The most surprising metric was time shift.

For weeks my correlation scores looked decent — around 65 to 70 percent. I thought that was reasonable. Then I added time shift detection.

It showed my model was consistently running 10 to 15 minutes ahead of the actual market.

The forecast shape was correct. The timing was off.

Once I knew that, I could account for it. Within two weeks my full day correlation jumped from 68 percent to 81 percent — not because my model got better, but because I finally understood how it was wrong.

You cannot fix what you cannot measure.

Running Multiple Models

The second insight came from comparing models side by side.

I run three different forecast approaches each morning. Before this tool I would look at them visually and pick the one that "felt" most reasonable.

Now I have a comparison table. Every metric. Every model. Automatically ranked.

Some days Model A wins on correlation but Model B wins on direction accuracy. Some days one model nails the morning session while another gets the afternoon right.

The table shows exactly where each model is strong and where it falls apart. That is information you cannot get from looking at lines on a chart.

The chart itself has full interactions — hover tooltips, crosshair, zoom, pan, timeframe switching from 1 minute to 30 minutes, moving averages. What the Hover Shows

When you move your cursor over the chart you see:

• Exact time label
• Live Nifty value at that minute (change from open)
• Each forecast model value at that minute
• Difference between actual and forecast in points

In the analysis table every cell highlights the best performer in green. You can see at a glance which model is winning, which metric each model leads, and what the composite score is right now.

What This Is Not

This is not a trading system. It does not give buy or sell signals.

It is a measurement and improvement tool. Its job is to tell me honestly how accurate my forecast was today — in 30 different ways — so I can understand my model better and improve it over time.

The goal is not to be right every day. The goal is to understand exactly how and when and why I am wrong, so the model gets better over time.

What Is Next

will update and have real time from Monday or whatever possible at earliest

The Bigger Point

Anyone can post a forecast. Very few people measure it rigorously.

If you are serious about market forecasting — intraday or otherwise — you need a measurement system as rigorous as your forecasting system.

Otherwise you are flying blind and calling it analysis.

Build the feedback loop. Measure everything. Improve systematically.

That is how forecasting becomes a skill rather than a guess.

I publish daily Nifty 50 intraday forecasts along with real-time accuracy tracking. Follow for updates on methodology, results and the ongoing development of this tool.

I don’t get it. Can’t get an answer from Support or their bot…

Been digging into prediction markets and ended up implementing LMSR (Logarithmic Market Scoring Rule) in Python.

It’s the mechanism that turns trades into prices, and I wanted to see it working end-to-end instead of just reading the math.

Repo if anyone wants to poke it: https://github.com/mwaleedta/lmsr-pricing-engine

Open to feedback or ideas for extensions (simulation, arbitrage, multi-market setups, etc.)

Polymarket, Kalshi and other prediction markets often let you trade the same real-world events, but with different odds and slightly different payoff rules. That means users either have to check multiple sites manually or risk overpaying for the same thing.

Because these sites often have hidden fees which vary by market and platform, users also have to manually calculate fees to make a true comparison.

I built LocksBet to aggregate prediction market events in one place and show when the same event is trading across multiple platforms, and how the prices vary after fees.

The hardest technical problem was matching markets reliably. Right now the system uses embeddings to find likely cross-platform matches (which was already a lot more accurate than I would have thought). It then uses language models to compare the actual market rules and determine whether they’re truly equivalent. I still manually verify matches both as a safety layer and to generate better training/evaluation data for the matching pipeline.

The site also continuously scans for new markets, so the matching process has to work on an ongoing stream of differently worded event titles and rule descriptions. It also uses pricing, date, and other data to more intelligently find likely matches.

I originally built this for finding arbitrage opportunities where the same event is mispriced across platforms (and the site still flags arbitrage opportunities), but it’s also useful to just find the best price before placing a trade.

I'd love any feedback on the site, the matching approach, or anything else.

Few weeks ago, I posted that I built a terminal to track petrol price. Since then we cleaned it up, made it global, landed an incredible angel investor, and are currently experimenting in forecasted prices based on the dataset we have. You can check it out at PetrolPrice.xyz - welcoming any feedback!

I’ve spent the last 2+ years building IMALI as a solo developer.
One feature I’m excited about is the Telegram bot, which streams paper trading activity from my OKX Spot and OKX Futures bots.
In this video you’ll see:
Live paper trade alerts
Entries and exits
Spot and futures activity
Profit/loss updates
Strategy decisions as they happen
I built it because I wanted users to see how the bots behave before risking real money.
The goal isn’t to promise unrealistic returns—it’s to make automated trading easier to understand through transparency and real-time notifications.
If you’re curious, I’d appreciate your feedback.
You can also try the one-click demo and paper trading yourself:
https://imali-defi.com
What would you want to see in a trading bot’s Telegram alerts that most platforms don’t provide?

Hey. I created a page where you see 1 pick of the trade. So far it has 88.9% win rate. One game a day where proven smart money has taken a side. Anyone want to give it a go?