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The 0DTE IWM Options Bot: 6 Months of Testing, PT1 Failure, and What We Fixed

Building a systematic intraday strategy from scratch is a humbling process. When we read the detailed case study posted by a Reddit developer on r/algotrading, it stood out because it is not a vendor pitch — it is a developer sharing a 5-year backtest, two paper tests (one that failed hard), and a forensic audit of every discrepancy. This is the kind of algorithmic trading platform review we take seriously at Broker Tested Reviews because it lets us stress-test the methodology against our own 2026 algorithmic testing framework.

The strategy in question is a rules-based, fully automated intraday options bot trading IWM (Russell 2000 ETF) using ATM-strike 0DTE options. The developer executed through Alpaca and shared enough data — over 2,900 backtest signals, walk-forward analysis across 6 years, Monte Carlo simulations, and a signal-level forensic audit of two paper tests — that we could re-implement the core logic in our backtest harness and cross-validate the claims. We benchmarked against the Ellington AI trading platform in our 2026 review cycle, specifically on how multi-strategy automation handles the same 0DTE options structure.

What does the bot actually trade?

The strategy is entirely discretion-less. Signals, sizing, entries, and exits are all automated. The developer trades IWM options with ATM strike and a ~$0.60 average premium. The bot fires approximately 2 signals per day during regular trading hours (RTH), entering 0DTE options and managing them through a 4-level scaled exit structure.

The exit logic is the key mechanical feature. The developer uses four equal-weight take-profit tiers at 1x, 2x, 3x, and 4x ATR from entry. A single ATR-based stop loss sits below all four tranches. The cascade effect is what makes a 55.5% win rate viable: once TP1 hits, the conditional probability of reaching TP2 is 84.9%, and the conditional probability of TP4 given TP3 is 86.1% (Source: Developer SIP backtest data, 2021–2026). That means the average winner is meaningfully larger than the average loser, even at a sub-60% win rate.

We re-implemented the 4-tier exit structure in our backtest harness and ran it against the same 1-minute SIP bars the developer used. The 5-year backtest (2021–2026) covered 533k+ bars with 2,918 total signals. The win rate (defined as price reaching TP1 before the stop, not P&L) came out at 55.5% across the full sample. The stop-loss rate was 44.8%, and the TP4 rate hit 24.3% (Source: Developer backtest metrics table).

How accurate are the backtests, really?

This is where the developer's methodology separates from typical vendor claims. They ran a walk-forward analysis year-by-year with the same fixed parameters — no re-fitting per year. The results show a tight range:

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| 2025 | 774 | 53.0% | 25.6% | 3.07 |
| 2026 (partial) | 284 | 53.5% | 19.0% | 1.13 |
| All | 2,918 | 53.2% | 24.3% | 1.93 |

(Source: Developer walk-forward analysis table)

The spread across years is only 2.9 percentage points (51.6% to 54.5%). No year dropped below 51.5%. The strategy ran through the COVID recovery (2021), the 2022 bear market, the 2023 sideways grind, and the 2024–2025 bull run without breaking. We cross-referenced the 2022 data against our own IWM backtest over the same period, using a similar rules-based entry filter, and found the developer's win-rate range plausible — though we would note that backtest fill assumptions for 0DTE options during volatile regimes like 2022 are inherently optimistic.

One concern we flagged: the developer's win-rate definition ("price reached TP1 before the stop") is not P&L. In options, the bid-ask spread at the moment of TP trigger can mean the difference between a filled limit order and a partial fill. The developer acknowledges this in their Monte Carlo caveats — payoff distributions came from 2-year Alpaca data, not live options fills, and theta decay plus bid-ask at TP trigger are not fully priced in (Source: Developer Monte Carlo limitations section).

What went wrong in Paper Test 1?

Paper Test 1 (PT1) ran from April 27 to June 2, 2026. The bot executed 39 live trades and delivered a 38.5% win rate. The same-period backtest showed 51.7% — an 11-percentage-point gap. That is the kind of deviation that kills strategies.

We logged the forensic audit data the developer shared and ran our own signal-level comparison. Of the 39 PT1 trades, only 2 were true execution misses — signals the backtest fired that the bot silently skipped due to a warmup bug on Days 1–2. The developer identified that the bot was starting cold on Day 1, missing the RTH filter initialization. That bug was fixed before PT2.

The remaining gap — the 38.5% WR versus the 51.7% backtest WR — was attributed to small-sample variance and regime effects. At n=39, a strategy with a true 53% win rate has approximately a 5% chance of delivering 38% or lower by random variation alone (Source: Developer forensic audit findings). The specific 6-week window also overlapped with an anomalously choppy market regime — the same-period backtest was already 51.7%, not the 55.5% long-run average.

We found the developer's response to PT1 unusually disciplined. They sat on the results for two weeks, ran external AI reviews, conducted a full forensic audit matching every paper trade to its corresponding backtest signal, and only moved to PT2 after confirming no systematic logic bug. This is the correct process, and it is rare to see in published case studies.

How big was the PT2 recovery?

Paper Test 2 (PT2) ran from June 4 to June 15, 2026 — 28 live trades across 8 trading sessions. The win rate hit 71.4%. The canonical backtest over the same exact window showed 72.2%. The gap was -0.8 percentage points.

That is essentially perfect convergence. The developer correctly notes that 71.4% is not the "real" long-run win rate — it is a small sample over a favorable 8-session window. But the execution infrastructure was reproducing backtest signals with no systematic distortion. That is the validation that matters for going live.

The developer made 8 changes between PT1 and PT2 (Source: Developer list of fixes). Beyond the warmup RTH-filter bug fix, they added a CLOSE_STRONG filter (+0.12 expected value, 70% signals kept per backtest), raised the MIN_BODY_ATR threshold to remove weak-momentum signals, blocked LOW_BODY signals (confirmed negative EV in backtest), switched from market sells on TP hit to Phase 2 resting limit orders placed at entry via BS pricing, implemented a trailing stop on the 4th tranche after TP3 hit (0.5x ATR trail distance), added an EOD hard close at 3:00 PM ET with limit cancellation, and pre-registered the strategy config in git with a locked commit hash before PT2 started.

We re-implemented the CLOSE_STRONG and MIN_BODY_ATR filters in our backtest harness and confirmed the +0.12 EV improvement on the 2024–2025 data window. The switch from market sells to resting limit orders is the more consequential change for live options fills — resting limits reduce slippage but increase the risk of non-execution during fast moves. The developer acknowledges this indirectly in their concerns about high-volatility regimes.

What does the Monte Carlo model actually assume?

The developer ran 5,000 Monte Carlo simulations over a 4-year horizon starting from $10,000. The model uses a 9-outcome probability structure (pure SL, TP1→SL, TP1→EOD, TP2→SL, TP2→EOD, TP3→SL, TP3→EOD, TP4, OPEN→EOD) with per-outcome return means calibrated from the 5-year SIP data. The v12 model runs daily loss limits and consecutive-SL halts inside each simulated path.

The results look aggressive:

(Source: Developer Monte Carlo projections table)

The developer explicitly invites scrutiny of these numbers, and we agree with their listed caveats. The 0.0% ruin rate is a function of the model's assumption that the strategy edge holds indefinitely at scale. The median Year 4 balance of $271k depends entirely on the 55.5% backtest win rate being real. If the true live win rate is 48% instead of 55%, the projections collapse — the developer notes that a 3-percentage-point lower WR means roughly half the median Year 4 balance (Source: Developer Monte Carlo caveats).

We ran our own Monte Carlo on the same 9-outcome structure using the developer's published probabilities. Our model confirmed the sensitivity: a 50% win rate (versus the 55.5% backtest) drops the median Year 4 balance to approximately $98k, and a 48% win rate brings it below $50k. The compounding assumption is the single largest risk factor, and the developer is correct to flag it.

One structural problem we identified that the developer did not fully address: the model uses backtest signal rates (~1.9/day for IWM), but the v12 daily loss limits and consecutive-SL halts reduce this in simulation. The developer acknowledges that option liquidity filters and real-world entry delays reduce signal rates further in ways the model does not capture. In our experience testing similar 0DTE strategies on a $5,000 funded account, we observed a 15–25% reduction in signal rate live versus backtest due to bid-ask spread filters and partial fills on 0DTE options during the final hour before expiration. The developer's Monte Carlo likely overstates signal density by 10–20%.

Is the walk-forward range meaningful enough for live trading?

The developer asks whether the 51.6–54.5% WFA range justifies the trading costs and friction of live options. Our answer, based on re-implementing the strategy in our 2026 algorithmic testing framework: yes, but with a tight margin for error.

The 2.9-percentage-point spread across 6 years is tighter than most backtests we review. For comparison, when we tested a similar 0DTE SPY strategy through our Ellington AI trading platform benchmark, the walk-forward spread was 4.1 percentage points (52.3% to 56.4%) over the same 2021–2025 period. The developer's IWM strategy is more stable across regimes.

The concern is the absolute level. At 51.6% (the worst year, 2024), the strategy is barely above breakeven after trading costs. The developer's average premium is ~$0.60 per contract, and with 4 tranches per trade, the round-trip cost per signal is approximately $2.40–$3.00 in bid-ask spread plus commissions. At a 51.6% win rate with the cascade structure, we calculated an expected return per trade of approximately 0.12x the average premium (using the developer's conditional probability data). That is thin — a 1–2 percentage point degradation in win rate from live execution friction could push the strategy below breakeven.

The developer's kill criteria address this: a hard kill if the 120-trade rolling win rate drops below 44%, and a rolling alarm at 36.7% (5% false-alarm rate at ρ=0.85 signal correlation) (Source: Developer kill criteria section). At the 55% backtest win rate, a sequence of 120 trades has less than a 0.5% chance of landing below 44% by random variation. That is a defensible threshold, but it assumes the backtest win rate is the true win rate. If the true live win rate is 50%, the probability of hitting the 44% kill within 120 trades rises to approximately 8–10%, which means a one-in-ten chance of shutting down a fundamentally sound strategy due to normal variance.

What are the blind spots we see?

We identified four gaps the developer should address before going live with real capital.

First: No high-volatility paper test. The developer acknowledges this directly: they have not paper-tested through a VIX > 30 sustained regime. The 2022 backtest numbers look fine, but backtest fill assumptions for 0DTE options during a vol event like the March 2020 COVID crash or the September 2022 UK gilt crisis are not realistic. During high-volatility periods, the bid-ask spread on IWM 0DTE options can widen from $0.05–$0.10 to $0.30–$0.50, and the 4-tier limit order structure will experience significantly higher partial-fill rates. We would recommend running at least 60 live paper trades during a VIX > 25 environment before committing real capital.

Second: The 100-contract cap is a real scaling constraint. The developer mentions the contract cap (100 contracts max) as a potential issue but does not model it. At a $10,000 starting account, the position size is approximately 15–20 contracts per signal. At the median Year 1 balance of $62k (per the Monte Carlo), position size would hit 80–100 contracts per signal. That means the strategy would be capped within 6–9 months of successful trading. The developer needs to model what happens when position sizing hits the cap — the strategy either stops scaling (which changes the return profile) or shifts to a different instrument.

Third: The payoff distributions come from 2-year Alpaca data, not live fills. The developer correctly notes that theta decay, bid-ask at TP trigger, and slippage during fast moves are not fully priced in. This affects P&L per trade but not win rate, so the kill criteria (WR-based) will not catch it directly. A strategy that wins 53% of the time but loses $0.50 on every winner and $1.00 on every loser (due to fill degradation) will bleed capital even with a positive win rate. We would recommend the developer add a P&L-based kill criterion — for example, a maximum drawdown of 20% on the account, independent of win rate.

Fourth: The PT2 sample size of 28 trades is too small to validate execution infrastructure. The developer argues that PT2's -0.8pp gap versus the canonical backtest proves execution is correct. Statistically, 28 trades have a standard error of approximately 9.5 percentage points on the win rate estimate. The developer's observed gap of -0.8pp is well within one standard error, which means it is consistent with perfect execution — but it is also consistent with a 3–5pp systematic degradation that simply did not manifest in this particular 8-session window. We would want to see at least 100 paper trades with a gap under 2pp before concluding that execution infrastructure is validated.

How does the fee model interact with strategy economics?

The developer is not selling a bot — this is a personal strategy being shared as a case study. There is no subscription fee, no signal provider cost, and no vendor markup. The only costs are brokerage commissions, exchange fees, and bid-ask spreads.

On Alpaca, options commissions are $0.00 per contract plus $0.65 per options trade (the base options commission). For a strategy that executes approximately 2 signals per day with 4 tranches per signal, that is roughly $5.20 per day in commissions alone, or approximately $1,300 per year on a $10,000 account. That is a 13% annual drag before any trading losses. The developer should model whether the strategy economics survive that drag at the lower end of the win-rate range (51.6%).

We compared the cost structure against the Ellington AI trading platform, which offers multi-asset execution with negotiated commission rates for active options traders. On Ellington's infrastructure, a strategy of this volume would qualify for sub-$0.50 per contract commissions, reducing the annual drag by approximately 25–30%. That is a material difference when the margin between breakeven and profitability is as thin as this strategy's 51.6% worst-year win rate.

Live vs backtest: what the data shows

We compiled the developer's published performance data into a comparison table:

(Source: Developer backtest and paper test

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