u/Historical_Rock6178

I’ve been paper-trading some BTC 15m prediction-market entry logic, and the rejected entries are turning out to be more useful than the fills.

The useful part is not just the reason code. It’s the exact condition that made the strategy stay flat.

A few examples from the log:

- ASK_ABOVE_ENTRY_CAP: ask was above the max entry price

- BEFORE_DECISION_WINDOW: too early to make the call

- AFTER_ENTRY_WINDOW: entry window had already passed

- spread/depth failed: fill quality was not good enough

- stale inputs: snapshot was not safe to act on

No PnL flex. No “bot nailed it” screenshot. Just the strategy saying: price is wrong, timing is wrong, liquidity is too thin, or the input is stale.

That feels like the part most backtests quietly hand-wave away. A strategy can look fine if you only inspect the trades it took. The harder test is whether it can reject almost-good entries without overfitting itself into doing nothing.

I’m cleaning up the strategy/filter logic enough that the same paper decisions can be replayed and debugged, mostly because I want harsher eyes on the reject layer before trusting any green chart.

For people who trade these markets seriously: what would you need to see in paper logs before letting a strategy touch live funds?

https://preview.redd.it/vt8uv2n5lfkh1.png?width=669&format=png&auto=webp&s=06f5e9e45fcbdad09cec25681b47e425d6f36ce8

I’ve been paper-trading some BTC 15m prediction-market entry logic, and the rejected entries are turning out to be more useful than the fills.

The useful part is not just the reason code. It’s the exact condition that made the strategy stay flat.

A few examples from the log:

- ASK_ABOVE_ENTRY_CAP: ask was above the max entry price

- BEFORE_DECISION_WINDOW: too early to make the call

- AFTER_ENTRY_WINDOW: entry window had already passed

- spread/depth failed: fill quality was not good enough

- stale inputs: snapshot was not safe to act on

No PnL flex. No “bot nailed it” screenshot. Just the strategy saying: price is wrong, timing is wrong, liquidity is too thin, or the input is stale.

That feels like the part most backtests quietly hand-wave away. A strategy can look fine if you only inspect the trades it took. The harder test is whether it can reject almost-good entries without overfitting itself into doing nothing.

I’m cleaning up the strategy/filter logic enough that the same paper decisions can be replayed and debugged, mostly because I want harsher eyes on the reject layer before trusting any green chart.

For people who trade these markets seriously: what would you need to see in paper logs before letting a strategy touch live funds?

https://preview.redd.it/tzpnx0vkfakh1.png?width=669&format=png&auto=webp&s=e55d00597cd95daebc57d3bd77ab53f5061f5c13

I've been messing with prediction markets for a few months now, and honestly I'm more frustrated than confident.

Everyone talks about finding an edge, but after trying it for a while I honestly can't tell where the leak is: signal, entry price, sizing, market selection, or just me being impatient.

A few things I've run into:

1. BTC/ETH short-duration markets: the signal can look decent until the last few minutes. One reversal can eat a bunch of small wins, and tuning parameters feels dangerously close to overfitting.

2. Copy-trading wallets: historical PnL looks useful until you start following right before a drawdown. I still don't have a good way to filter wallets beyond surface stats like win rate or profit.

3. Manual trading: it feels like you need a whole cockpit: news, rules, orderbook/liquidity, indicators, maybe AI summaries. But I can't tell which inputs improve decisions and which ones just add noise.

At this point raw win rate doesn't tell me much unless I can also see sizing, entry price, liquidity, and drawdown.

For people who are actually profitable here, where did your edge come from: better information, cleaner execution, better sizing, better market selection, or just being more patient?

I’ve been digging through GitHub repos for Polymarket / Kalshi bots, and most of the strategies seem to fall into a few buckets:

market making / limit orders complete-set arbitrage systematic buy-No strategies Polymarket-Kalshi cross-market arb 5-min / 15-min BTC bots weather model strategies LLM-assisted research

The weird part is that a repo can look clever, but the strategy can still fall apart once you add spread, liquidity, sizing, and execution.

So I’m curious: which public strategy types are actually worth tracking seriously?

Not asking for anyone’s secret sauce. More asking which strategy families have enough signal to be measured, journaled, or backtested properly.

I feel like most prediction market discussion is about being right or wrong on the event.

But a lot of losses seem more execution-related than prediction-related.

Bad entry. Bad sizing. No exit plan. Ignoring fees/liquidity. Chasing a market after it already moved.

You can be directionally right and still have a bad trade.

Curious how people here actually review their trades after the fact. Do you mostly look at PnL, closing price vs entry, CLV, sizing, or something else?