Strike Distance

BTC 15-Minute Strike Confluence: Tightening the Spot-vs-Strike Distance Gate

Turbine research note — Kalshi crypto, historical simulation

Short disclaimer

This is historical simulation research, not investment advice and not a claim about future performance. Results below come from a single 30-day backtest per variant and should not be read as forecasts.

Intro / thesis

The BTC 15-minute strike confluence strategy on Kalshi trades short-dated contracts when spot has moved a meaningful distance away from the strike, in the last five minutes before expiry, and when short-term momentum agrees with the side being taken. The current setup requires spot to sit at least $250 from the strike before it will enter. The question in this batch: does that $250 band leave money on the table by being too strict? We compared the current $250 baseline against $200, $180, $150, and $130, holding every other rule fixed.

A lower threshold admits more setups, since a smaller spot-strike gap clears the gate. So the trade-off is roughly "more trades and maybe lower win rate" versus "fewer trades and maybe cleaner entries." On paper, you would expect a higher bar to mean higher win rate per trade and a lower bar to mean more volume. The results do not fall neatly into that pattern.

Variant and strategy explanation

All five variants share the same rules and differ only in the strike-distance number. Each is a Kalshi custom strategy on the KXBTC15M series. The engine evaluates every 10 seconds, position size is 50 contracts, and the price gate for the traded side sits between $0.45 and $0.99 with a spread cap of $0.02.

The mechanics, in order:

• Exit on a flip against the position. If holding YES and spot-vs-strike goes below 0, sell YES. If holding NO and spot-vs-strike goes above 0, sell NO.
• Entry on the YES side. Requires no current position and no prior orders, spot above zero, five minutes or less to expiry, spot-vs-strike at or above the distance gate, spot above the 1-hour VWAP, 5-minute change positive, 1-minute velocity at or above zero, YES ask between $0.45 and $0.99, and spread at or under $0.02. Buys 50 YES.
• Entry on the NO side. Mirror image: spot-vs-strike at or below the negative of the gate, spot below VWAP, 5-minute change negative, velocity at or below zero, NO ask between $0.45 and $0.99, spread at or under $0.02. Buys 50 NO.
• Entries are capped at one per market. The $250 max-loss figure is a halt-and-liquidate threshold on this strategy's own inventory, not a general exit rule.

The five variants set the strike-distance gate to 250 (baseline), 200, 180, 150, and 130. That single number is the only difference across the batch. Each completed variant has been saved as a runnable Turbine strategy, so any of the five can be re-run or extended.

Top results

Ranked by the batch's ranking metric, the tighter gates came out on top.

The $130 variant admitted the most trades (300) and made the most money in absolute terms, though at the cost of the batch's largest drawdown (-$47.04) and lowest win rate (94.7%). The $150 and $180 variants sit in between: fewer trades, higher win rates, smaller drawdowns, and lower total P&L. This is the monotonic pattern you might expect from loosening the gate — more volume, more total profit, somewhat worse per-trade quality and deeper drawdowns.

One thing worth flagging honestly: the $130 variant has both the deepest drawdown and the highest total P&L, and the $150 variant has a lower drawdown (-$27.24) than the $180 variant (-$11.27) despite trading more. The batch data does not explain these particular orderings, and they remain unresolved. The table above is what the simulation returned, not a story about why.

Bottom results

The current $250 baseline ranks last on the batch's metric, mostly because it took only 76 trades over the 30-day window — far fewer than any of the tighter variants. Its win rate was 100% and its drawdown was just -$0.88, which is very close to no drawdown at all. A 76-trade sample going 76-for-76 is a small, clean sample, and the data does not tell us whether that streak would persist at larger volume.

The $200 variant is a middle case: more trades than the baseline (136 vs. 76), a slightly lower win rate, and more total P&L. Its -$11.27 drawdown is identical to the $180 variant's -$11.27. Whether that match is coincidence or an artifact of how the two runs overlapped is not resolved by this batch.

Note also that the $250 baseline is ranked last despite being the current production setting. That does not mean the baseline is a bad strategy; it means that on this ranking metric, over this one window, four looser gates scored higher.

Conclusion

Within this 30-day historical backtest, tightening the spot-vs-strike gate from $250 to $130 produced the highest total P&L and had the best batch ranking, with $150 and $180 following in order, and $200 ahead of the $250 baseline. Loosening the gate admitted more trades pretty much monotonically (76 → 136 → 166 → 224 → 300 as the gate went 250 → 200 → 180 → 150 → 130), which is the intuitive direction.

The trade-off is real: the baseline took the fewest trades and had the smallest drawdown, while the tightest gate produced the most profit but the largest drawdown and the lowest win rate. Whether that trade-off is worth taking depends on position sizing, risk budget, and how much you trust a 30-day sample.

Caveats, stated plainly:

• This was one 30-day window per variant. No sweep, permutation test, or out-of-sample validation ran.
• The batch's ranking metric rewarded P&L heavily, so variants that traded more had more chances to accumulate profit. A different metric, like Sharpe-weighted or drawdown-adjusted, would reorder the list.
• Several patterns in the numbers (drawdowns tying across variants, the $150 drawdown being larger than the $180 drawdown despite more trades) are not explained by the batch output and remain unresolved.
• The 100% win rate on the baseline is a small-sample result and should not be read as reliable.

Each variant has been saved as a runnable Turbine strategy, so anyone wanting to test these gates on a different window or risk budget can do so directly. Nothing here suggests these results will repeat on future Kalshi markets.

Long disclaimer

This report is historical simulation research produced by Turbine. It describes backtested performance of algorithmically defined trading strategies on historical Kalshi market data over a single 30-day window per variant. It is not investment advice, not a solicitation, not a recommendation to buy or sell any security or contract, and not an offer to manage money.

Past simulated performance is not indicative of future results. Backtested results are subject to a long list of well-known limitations: they are derived from historical data that may contain errors, gaps, or survivorship issues; they assume fills at prices that may not have been achievable in live trading; they assume no significant market impact; they do not reflect fees, slippage, or funding costs unless stated; and they are sensitive to the specific window chosen. The variant rankings in this report reflect one window and one ranking metric. Different windows, different ranking metrics, or different risk budgets could produce different conclusions, including ranking the current baseline first.

The five variants differ only in the strike-distance threshold (250, 200, 180, 150, 130). Every other rule is identical, as documented in the strategy explanation. The simulation engine evaluates every 10 seconds. Entry orders are subject to a one-per-market cap and may be skipped or partially filled depending on live conditions. The $250 max-loss setting halts and liquidates only this strategy's attributed inventory; it does not protect against losses elsewhere or guarantee that any loss will be limited to $250.

Nothing in this report should be construed as a claim about live trading outcomes. No forward test, out-of-sample validation, permutation test, or robustness check was performed. Readers should treat all figures as descriptive of a single historical sample.

Turbine is a research platform. We do not manage client funds. You are responsible for your own decisions and for understanding the risks of trading event contracts, which can lose value rapidly and entirely.

If you choose to run any of these variants on a Turbine account, do so with capital you can afford to lose, at a position size consistent with your risk tolerance, and with the expectation that live results may differ materially from

About this Kalshi strategy backtest

What question does Strike Distance investigate?

Does a tighter spot-vs-strike distance gate improve the BTC 15-minute strike confluence strategy? Compare the 250-dollar strike distance used today against 200, 180, 150 and 130, with every other rule held fixed.

Read the analysis alongside the individual variant results. Check which entry, exit, price-band or risk settings changed, which settings stayed fixed, and whether the report documents enough historical coverage to support its comparison. A different parameter value defines a different simulation; it does not establish a new source of live trading returns.

How many strategy variants are included?

This report lists 5 completed and 0 failed strategy variants. Failed comparisons do not count as successful tests with zero profit. Review trade counts and drawdown alongside net P&L, win rate and Sharpe, and read any coverage or execution caveats in the analysis. A result based on very few trades needs particular care, even when its return looks high.

Does a profitable Kalshi backtest predict live performance?

These are historical simulations. Live results can differ because of liquidity, fees, slippage, latency, market resolution, outages and data quality. Selecting the best result after comparing many variants can also overstate how reliably it will perform on new data. Compare nearby settings, inspect losing variants and test on a separate period before considering a live run. The report is research, not a promise of profit or investment advice.

How can I inspect or test a strategy from this report?

Use an available Open strategy link to inspect that variant. Review its rules, market, date window, fill assumptions, fees and trade history before changing parameters or rerunning it. Confirm which data and execution assumptions the selected market actually supports; a replay does not establish that every historical order could have filled live. For the workflow, read the Kalshi and prediction market backtesting guide. The Kalshi backtesting software page explains the product's supported testing workflow.