Finding the "golden key" for prediction markets through 27.73 million transaction data points, yet 690 K-line strategies struggle to turn a profit

Author: Frank, PANews

How difficult is it to find a profitable “golden key” in prediction markets?

On social media, you often see people claiming to have discovered a clever money-making secret, but in reality, they say nothing substantial. What people can see are only the profit curves of these smart funds’ growth, not the underlying logic.

How exactly can one build a personalized, prediction-market-specific trading strategy?

Using BTC 15-minute prediction markets as an example, PANews analyzed nearly 27.73 million trades across 3,082 windows over the past month and arrived at some conclusions that may challenge conventional understanding. In previous articles, we already examined the macro data of this market. This time, we will delve deeper into the details to find that possible “golden key.”

Illusion shattered: The complete failure of candlestick technical analysis

Have you ever considered a strategy that treats prediction markets like stocks or cryptocurrencies, analyzing only different entry and exit points, combined with position management, take-profit, stop-loss, and other factors, to develop a trading approach completely independent of BTC price movements—focusing solely on predicting market price changes?

In traditional crypto markets, this approach is known as “technical analysis.” Theoretically, it should work equally well in prediction markets. Therefore, PANews simulated this approach and developed a custom backtesting system for prediction markets. This system allows inputting entry points, take-profit and stop-loss levels, entry timing, and filtering out interfering prices to evaluate the actual profit/loss ratio and win rate based on over 3,000 markets from the past 30 days.

Initially, with incomplete data (Polymarket’s historical data provides only 3,500 entries per market), the backtest easily found profitable results—such as entering at 60% of the price, selling at 90%, stopping loss at 40%, and trading within a set window.

But real-world results diverged sharply. Under actual execution, the profit curve declined slowly, like a dull knife cutting meat. We then tried to supplement the data as much as possible, eventually obtaining price information for all markets. This time, the results finally aligned with reality.

With real data, PANews simulated 690 combinations of price, take-profit, stop-loss, entry timing, interference filtering, and slippage. The final conclusion: no strategy could achieve a positive expected return.

Even the most promising one had an expected return of -26.8%. This indicates that, in prediction markets, pure mathematical forecasts that exclude event-specific factors are almost impossible to profit from.

For example, the widely discussed “end-of-day strategy” on social media—buy at 90%, sell at 99%. It seems to have a very high win rate and be profitable in the long run. In our simulations, this strategy achieved a win rate of 90.1%, with 2,558 out of 3,047 trades hitting take-profit. But alarmingly, the actual profit/loss ratio was only 0.08, and the expected value based on the Kelly criterion was -32.2%, making it not worth adopting.

Some might ask: would adding a stop-loss improve the profit/loss ratio? The harsh reality is that increasing the profit/loss ratio often reduces the win rate. For example, setting a 40% stop-loss drops the win rate to 84%. Given the still low profit/loss ratio, the Kelly expected value remains negative at -37.8%, still losing money.

The most close-to-profitable approach appears to be a reversal bet—buying at 1% below the current price, betting on a market reversal. In simulations, this approach has a win rate of about 1.1%, higher than the probability implied by the price, and an extremely high profit/loss ratio of 94, leading to an expected return of 0.0004. But this assumes no slippage or fees; once transaction costs are included, the expected value instantly turns negative.

In summary, our research shows that relying solely on technical analysis from traditional finance to profit in prediction markets is futile.

“The double-sided arbitrage” trap

Besides these strategies, a mainstream view is that of “double-sided arbitrage”: as long as the total cost of YES + NO outcomes is below 1, profit is theoretically guaranteed. This idea sounds appealing but is overly idealistic.

First, with current cross-platform arbitrage bots, ordinary users cannot compete with the high liquidity provided by these bots.

Second, one could attempt to buy when the YES and NO prices both drop to 40%, aiming for a 20% arbitrage margin. But data shows that, although such a strategy has a 64.3% success rate, its low profit/loss ratio results in an overall negative expected value.

This “double-sided strategy” looks good in theory but is prone to failure in practice. It also falls into the category of pure theoretical setups detached from actual event dynamics.

The “Fair Value” and deviation models are the real “golden keys”

So, what kind of strategy might actually be profitable?

The answer lies in the “time lag” between BTC spot prices and prediction market token prices.

PANews found that liquidity providers and market maker algorithms in prediction markets are not perfect. When BTC experiences sharp moves within a short period (e.g., 1-3 minutes), such as a sudden jump of over $150 or $200, the token prices in the prediction market do not instantly “jump” to their theoretical values.

Data shows that this pricing “inefficiency” diminishes from a maximum of about 0.10 to half that (around 0.05), typically taking about 30 seconds.

Thirty seconds may be a lifetime for high-frequency traders, but for manual traders, it’s a fleeting “golden window.”

This indicates that prediction markets are not perfectly efficient. They are more like sluggish giants; when BTC’s trend is already set, they often lag behind by half a beat.

However, this does not mean that only fast reflexes can profit. Our data further shows that this “delay arbitrage” space is shrinking rapidly. In small BTC price movements under $50, after deducting gas fees and slippage, most so-called “arbitrage opportunities” are actually negative expected value traps.

Beyond momentum trading based on speed, PANews’s research reveals another profitable logic based on “value investing.”

In prediction markets, “price” does not equal “value.” To quantify this, PANews built a “Fair Value Model” based on 920,000 historical snapshots. This model does not rely on market sentiment but instead calculates the theoretical probability of success based on BTC’s current volatility state and the remaining time until settlement.

Comparing the theoretical fair value with the actual market price reveals the nonlinear nature of prediction market pricing efficiency.

1. The magic of time

Many retail traders intuitively believe that prices should converge linearly over time. But data shows that convergence accelerates.

For example, under the same BTC volatility conditions, the price correction speed in the last 3-5 minutes of a market is much faster than in the first 5 minutes. Yet, markets often underestimate this convergence speed, leading to significant deviations where token prices in the later stages (remaining 7-10 minutes) are often well below their fair value.

2. Only “deep discounts” are worth buying

This is the most important risk control conclusion of this study.

Backtesting different deviation levels (Fair Value - Actual Price) shows:

When the market price exceeds the fair value (i.e., a premium buy), regardless of BTC trend, the long-term expected value (EV) is negative.

Only when the deviation exceeds 0.10—that is, the actual price is at least 10 cents below the fair value—does the trade have a robust positive mathematical expectation.

This means that for smart capital, a quote of $0.70 does not imply a “70% chance to win”; it’s just a quote. Only when the model calculates a true success probability of around 85% at that price does it become a “cheap” bet worth making.

This also explains why many retail traders lose money in prediction markets: their actual transaction prices are often above the true fair value, leading to buying at a premium.

For ordinary participants, this research serves as a sobering reality check and an advanced guide. It tells us:

Abandon candlestick superstition: don’t try to find patterns in prediction token charts—that’s an illusion.

Focus on the underlying asset: watch BTC’s movements, not just the prediction market.

Respect odds: even with a 90% win rate, if the price is too high (premium), it’s a doomed trade.

In this algorithm-driven jungle, unless retail traders can establish a “fair value” mathematical coordinate system and possess the technical ability to capture the “30-second lag,” every “Buy” click might just be a donation to the liquidity pool.