XRP Entry Window

XRP 15-Minute Entry-Window Study (Kalshi)

Disclaimer (short)

This is historical simulation research on a small batch of variants, not a live trading record. It is not investment advice, and nothing here should be read as a claim about future profits.

Intro / thesis

The base strategy trades Kalshi's 15-minute XRP markets and only looks for entries very close to expiry, where prices are already pinned near the extremes (0.90–0.99). The question in this batch was narrow: does tightening the entry window from the last 50 seconds to the last 45 or 40 seconds change anything meaningful, with every other piece of the strategy held constant?

Everything else was left alone: the 0.90–0.99 entry band, the momentum stack (price vs. 1-hour VWAP, 12-period EMA vs. 20-period SMA on 1-minute bars, and 1-hour/15-minute/5-minute changes plus 1-minute velocity all agreeing on direction), the late entry rules that only need a 1-cent spread and velocity in the right direction, the 50-cent stop, 10-contract size, and the 10-contract position cap.

Three variants were completed out of three planned. Each candidate has its own complete rule set, market, and window; the base strategy is context only. All three were run over a 30-day simulation period.

Variant and strategy explanation

The mechanics are the same across all three candidates, evaluated every 10 seconds, top-to-bottom with the first matching rule ending that tick:

• Cancel resting orders when there are open orders and no position (order_count > 0 and position_size == 0 → cancel_all).
• Stops: with a YES position, sell when the YES best bid is at or below 0.50; with a NO position, sell when the NO best bid is at or below 0.50.
• Full-momentum entries (rules 4 and 5): require flat position, time to expiry inside the variant's window, spread ≤ 0.03, ask between 0.90 and 0.99, and the full momentum stack aligned — price above 1-hour VWAP, 12 EMA above 20 SMA, and 1-hour, 15-minute, 5-minute changes and 1-minute velocity all positive for YES (all negative for NO). Buy 10 contracts.
• Late entries (rules 6 and 7): require flat position, time to expiry inside the window and ≥ 0, spread ≤ 0.01, ask between 0.90 and 0.99, and only 1-minute velocity in the right direction. Buy 10 contracts.
• Settle exit: cancel all at time to expiry ≤ 0.

The only change between variants is the time_to_expiry threshold in those entry rules:

• 50s variant: entries allowed at ≤ 50 seconds to expiry.
• 45s variant: ≤ 45 seconds.
• 40s variant: ≤ 40 seconds.

Each successful variant is saved as a runnable Turbine strategy:

• 40s: xrp-entry-window-68ac959e7edb
• 50s: xrp-entry-window-b8b1d61b62a5
• 45s: xrp-entry-window-f57a10b9eca2

Top results

The 40-second window stands out. It took fewer trades (90 vs. 124), won more of them (97.8% vs. 90.3%), made roughly twice the total PnL, and did it with a far smaller worst drawdown (-2.33 vs. -14.00). Its Sharpe of 0.81 is also well above the others, though 0.81 is still a modest number in absolute terms. In plain terms: waiting a bit longer before allowing entries appears, in this simulation, to have filtered out the marginal trades that were losing money and adding drawdown.

Bottom results

The clear bottom of this batch is the 45-second variant, which lands in last place with 124 trades, a 90.3% win rate, 10.08 total PnL, 100.8% ROI, -14.00 max drawdown, and a 0.12 Sharpe. The 50-second variant produces identical numbers across every reported metric and ranks second.

That identical output is a notable feature of this batch: the 45s and 50s variants have exactly the same trade count, win rate, PnL, ROI, drawdown, and Sharpe. Nothing in the data provided explains why a five-second difference in the entry window would produce identical results while the 40s window differs so sharply. I am not going to invent a mechanism for it — whether this reflects how the simulation sampled entries, rounding in the reporting, or something else, the discrepancy is unresolved and should be treated as such.

The comparison between windows is also not uniform: moving from 50s to 45s changed nothing in the output, while moving from 45s to 40s changed everything. That pattern is worth flagging rather than smoothing over, especially given the small sample (90–124 trades over 30 days) and the fact that only three variants ran.

Conclusion

Within this single 30-day batch, the 40-second entry window was the best of the three by every reported measure — more PnL, higher win rate, smaller drawdown, higher Sharpe — and is saved as a runnable Turbine strategy. The 50s and 45s variants tied for the bottom with identical results, and the reason for that exact tie is unresolved.

A few caveats matter more than the ranking. This was a three-point comparison, not a sweep; no permutation or robustness testing ran, so the winner's edge cannot be assumed to be more than noise from this sample. The 30-day period is short for strategies that depend on precise late-window pricing, and results at these price levels are sensitive to fill assumptions. The near-98% win rate on the 40s variant is impressive in isolation but also the kind of figure that deserves skepticism until it holds up over longer, different periods and with a realistic execution model. I would not extrapolate from this batch to future performance, and I would want the 45s/50s tie explained before drawing conclusions about the sensitivity of the strategy to the entry window at all.

Disclaimer (long)

This report describes historical simulation research conducted on Kalshi 15-minute XRP markets using a 30-day lookback. All figures are simulated backtest outputs, not records of live trading, and backtests are subject to known limitations including but not limited to: assumptions about order fills, queue position, latency, and available liquidity; the possibility of lookahead or survivorship effects in the underlying data; and the fact that market conditions in the test period may not resemble those in any future period. The simulation evaluated a small explicit batch of three variants of a single strategy family on a single market; no parameter sweep, cross-validation, out-of-sample holdout, permutation test, or statistical significance testing was performed. The ranking of variants should not be read as evidence of a durable edge, and the winner of this batch is not a forecast. The identical reported results for the 45-second and 50-second variants are unexplained by the information available and should not be treated as a stable finding. Cryptocurrency markets, and short-dated event contracts on them, can move sharply and liquidity can disappear precisely when it is most needed; high simulated win rates on contracts priced near 0.90–0.99 reflect frequent small gains that can be erased by a small number of large losses, including losses beyond the modeled stop. Past performance, simulated or actual, does not guarantee future results. Nothing in this document is investment, legal, tax, or financial advice, nor a recommendation to buy or sell any instrument. Anyone deploying a strategy like this should independently validate it with realistic execution assumptions, appropriate position sizing, and risk limits they can afford to lose. Turbine makes no representation that any saved strategy will perform in live markets as it did in simulation.

About this Kalshi strategy backtest

What question does XRP Entry Window investigate?

Compare three entry-window lengths on the saved XRP 15-minute strategy: entries only inside the last 50, 45, or 40 seconds before expiry, with everything else (0.90-0.99 entry band, momentum stack, late 1-cent-spread rules, 50-cent stops, 10-contract size, 10 max position) unchanged.

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 3 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.