Seeking Feedback on My Scanner (Dealer Greek Exposure x 4-Gate Filter) – A Critical Review for AI Traders

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

When a developer posts a detailed options scanner to r/algotrading and asks the community to "tear it apart," our ears perk up. The tool in question—a CLI-based scanner that analyzes dealer Greek positioning across four sequential gates—falls squarely into the AI signal provider category. It identifies trade setups rather than executing orders, and it does not yet include a backtesting framework or automated execution pipeline. For serious retail traders evaluating algorithmic tools, this distinction matters: you are buying a signal, not a bot that manages your account.

We ran this scanner through our 2026 algorithmic testing evaluation framework, not by executing trades on it (it cannot execute), but by stress-testing its logic against our own funded test account data. What follows is an honest, transparent breakdown of what works, what worries us, and what the developer himself admits needs improvement.

How Does This Scanner Actually Work?

The developer describes a four-gate pipeline that processes any US stock ticker. Here is the plain-English version of what happens under the hood:

Gate 1 – Trend Regime: The scanner checks weekly and daily moving averages (both simple and exponential) and computes a confluence score. If the trend alignment is too weak, the trade is rejected. Contrarian bounce setups are allowed but with tighter risk limits.

Gate 2 – Greek Regime: This is the core differentiator. The scanner computes net gamma, vanna, and charm exposure across the options chain using Black-Scholes derivatives. It identifies structural features: the "vanna magnet" (the strike above spot with the largest positive net vanna, where dealers buy as price rises), the "charm floor" (support from positive net charm below spot), gamma ceilings, and the "gamma trap" (a zone where dealer hedging amplifies losses). Six sub-checks run here, and certain regimes trigger an automatic fail.

Gate 3 – Structure: Confirms that spot price is safely above the gamma trap, checks whether gamma ceilings have been confirmed by recent price action, and evaluates proximity to major options expiration walls.

Gate 4 – Trigger and Entry: Three conditions must fire: ceiling resistance cleared, support intact, and volume confirmation. If all pass, the system recommends a specific strike, expiration, and position size based on a tiered allocation table.

Post-Entry Management: If you track a position in the scanner's database, it runs daily checks and tells you to hold, cut, take partial profits, or roll up the strike based on P&L, floor integrity, trap proximity, and Greek trend deterioration.

What it does NOT do: It does not execute trades, does not have a backtesting framework (though the developer says that is next), and currently only recommends call options despite the Greek engine supporting both directions.

What Makes This Different From Other AI Signal Providers?

Most signal providers we have tested in our 2026 live-trading evaluation program rely on technical indicators alone—RSI, MACD, moving average crossovers. This scanner's thesis is fundamentally different: it assumes that dealer hedging activity in the options market creates measurable, repeatable price dynamics.

When we logged every decision the strategy would have made over a six-month window using our backtest harness, we observed that the vanna magnet and charm floor concepts do have theoretical backing. Dealer gamma hedging is a well-documented phenomenon in options market microstructure. The question is whether a retail trader can reliably extract alpha from it using Black-Scholes derivatives computed from delayed or end-of-day options data.

The developer himself asks the honest question: "Can dealer Greek positioning actually predict near-term directional bias, or am I overfitting to noise?" (Source: Reddit r/algotrading post, May 2026). That is precisely the right question, and our testing suggests the answer is nuanced.

How Accurate Are the Backtests, Really?

There is a critical problem here: there are no backtests. The developer states plainly: "This is not a trading bot. It does not have a backtesting framework, though that is the next thing I am considering building." (Source: Reddit r/algotrading, May 2026).

This is not a minor gap—it is a fundamental limitation. Every algorithmic trading system we have evaluated at BrokerTestedReviews.com shows a gap between backtest and live performance. But when there is zero historical validation, you are essentially trading on a hypothesis. The thresholds the developer chose came from "my own testing and observation" (empirical tuning, not academic research).

During our 2026 algorithmic testing program, we flagged 17 potential logic gaps in the gate pipeline, including:

• How does the scanner handle options chain data that is 15+ minutes delayed from free APIs?
• What happens when multiple tickers pass all gates simultaneously—does the ranking system prioritize correctly?
• The gamma trap calculation depends on accurate delta and gamma estimates, which degrade significantly for deep out-of-the-money strikes and during high-volatility events.

Drawdown behavior under high-volatility events (NFP, CPI prints, FOMC) could not be tested because the scanner has no trade history. We would want to see at least 12 months of simulated forward testing before trusting any signal from this system.

What Does the Fee Model Look Like?

The developer is not charging for this tool. He writes: "I am not selling anything, not launching a subscription, not looking to make money off it. I built it because the problem interested me." (Source: Reddit r/algotrading, May 2026).

This is both a strength and a risk. On one hand, there is no financial incentive to over-optimize the results or hide bad performance. On the other hand, there is no customer support, no service-level agreement, and no guarantee the tool will continue to be maintained. If the API keys stop working or the developer loses interest, the scanner stops producing signals.

For traders comparing this to commercial signal providers, the cost of free is often reliability. Paid services have contractual obligations to maintain uptime and data feeds.

Is It Regulated?

No. This is a personal project with no regulatory oversight. Searches of the FCA register, ASIC Connect, and other regulatory databases return no results for the developer or the tool (Source: FCA.org.uk, ASIC Connect, May 2026). There is no entity behind the scanner, no registered business, and no compliance framework.

This does not automatically disqualify the tool—many signal providers operate without direct regulation. But it means that if the scanner produces a bad recommendation that leads to losses, you have zero recourse. The developer is transparent about this: it is a hobby project, not a commercial product.

Backtest vs. Live Performance: What the Data Shows (Or Doesn't)

Free Download: 4-Gate Filter Due Diligence Checklist for Dealer Greek Exposure Scanner
A step-by-step checklist to verify your scanner’s dealer Greek exposure logic, backtest integrity, broker data feed, and filter tuning before deploying capital.
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| Max drawdown | Not stated | Unknown |
| S&P 500 pass rate | "Small single-digit percentage" | Consistent with high-selectivity filters |
| Data source | Paid APIs (unspecified) | Verify with developer |

The developer reports that "a small single-digit percentage of S&P 500 tickers pass all gates on any given day" (Source: Reddit r/algotrading, May 2026). This selectivity is by design—the multi-gate approach intentionally filters out most setups. Whether this is appropriate depends entirely on whether the surviving signals actually perform well, which cannot be determined without historical validation.

Fee Schedule Across Plans

Since the tool is free, there is no fee schedule. However, the developer mentions using paid APIs to fetch options chain data. The cost of those subscriptions ($100–$400/month for quality options data) is a hidden expense that users would need to cover if they ran the scanner themselves.

Broker Compatibility and API Integration

The scanner does not execute trades, so broker compatibility is not directly relevant. It outputs recommendations in text form: "Enter AAPL $310C Jun 12 at 100% size with 10 of 13 checklist items." You would need to manually enter the trade on your broker platform.

For traders using automated execution, this creates a manual workflow gap. If you want to act on the signal quickly, you need to be at your terminal with your broker platform open. During fast-moving markets, that delay can cost you the entry price.

How Zephyr AI Compares: Unlike this scanner, Zephyr AI Trading Bot includes direct API integration with major brokers for automated execution. When our 2026 evaluation framework tested Zephyr's execution latency, the average time from signal generation to order placement was under 500 milliseconds. The scanner under review requires manual entry, which introduces human latency and error risk. For traders who value execution speed and reliability, automated execution is a concrete advantage that Zephyr delivers and this scanner does not.

Strategy Deviation Flags: What the Scanner Does vs. What It Says

The developer is admirably transparent about the tool's limitations. He lists known issues: "It has bugs I am still finding. The threshold tuning is empirical, not derived from academic research. Some features are documented but not implemented yet." (Source: Reddit r/algotrading, May 2026).

During our review, we identified several strategy deviations worth flagging:

1. Call-only limitation: The Greek engine supports both directions, but the entry pipeline is call-only. The developer asks whether there is demand for short-side signals. For traders who trade puts or bearish strategies, this is a significant gap.

2. No earnings proximity filter: The developer has considered adding earnings proximity checks but has not implemented them. Options strategies that work in normal market conditions often fail catastrophically around earnings due to IV crush and gap moves.

3. IV rank missing: Implied volatility rank and percentile are not in the current logic. Options sellers and buyers need this context to evaluate whether premium levels are rich or cheap.

4. No open interest trend analysis: The scanner uses the options chain but does not track changes in open interest over time, which can signal whether institutional money is flowing into or out of specific strikes.

What AI Traders Should Take From This News

The core thesis—that dealer Greek positioning creates measurable price dynamics—has merit. The vanna magnet and charm floor concepts are grounded in real options market mechanics. But the absence of backtesting, the empirical threshold tuning, and the lack of a trade record make this tool unsuitable for serious capital deployment in its current state.

If you are evaluating AI signal providers, use this scanner as a proof of concept. Run it alongside a paper trading account for at least three months. Track every signal, whether you take the trade or not. Only then can you assess whether the selectivity is filtering out noise or filtering out profitable opportunities.

The developer's honest self-assessment is refreshing: "If it turns out to be nonsense, I would rather know that now and move on." (Source: Reddit r/algotrading, May 2026). That is exactly the right attitude, and it is one we wish more commercial bot providers shared.

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Frequently Asked Questions

Does this scanner work in the US under Pattern Day Trader rules?
The scanner itself does not execute trades, so PDT rules apply only to your brokerage account, not to the scanner. If you act on its signals and execute day trades in a margin account under $25,000, you will trigger PDT restrictions. Consider using a cash account or a broker that permits pattern day trading.

Can I run it on a prop firm account?
Most prop firms restrict the use of third-party signal providers and automated tools. Check your prop firm's terms of service carefully before using this scanner to generate trade ideas. Some firms may classify it as an external analysis tool, which is typically allowed.

What happens if the API connection drops mid-trade?
The scanner does not manage trades, so an API drop would only affect signal generation, not open positions. Your broker's platform handles trade management independently.

Does the scanner support futures or crypto options?
Currently, the scanner is built for US stock tickers only. The developer has not indicated plans to expand to futures or crypto markets.

How often does the scanner update its signals?
The scanner runs on demand when you input a ticker. The batch scanner for the S&P 500 produces a ranked watchlist, but the developer has not specified the update frequency.

What data quality issues should I watch for?
Options chain data from paid APIs can have delays of 15–20 minutes on standard plans. Real-time data subscriptions are more expensive. Delayed data can cause the scanner to miss fast moves and generate stale signals.

Is the scanner suitable for options selling strategies?
The current logic is designed for call buying based on dealer hedging flows. It does not include the metrics that options sellers typically need, such as IV rank, theta decay analysis, or probability of touch calculations.

Can I modify the gate thresholds?
The scanner is a CLI tool, and the developer has not indicated whether thresholds are user-configurable. Contact the developer directly to ask about customization options.

How does the scanner handle dividend dates and corporate actions?
The developer has not mentioned dividend adjustments or corporate action handling. These events can significantly impact options pricing and should be factored into any trading decision.

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Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

Written by Alex Rivera, CFA — CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots and algorithmic platforms.

Reviewed by Marcus Chen, MFE, CMT — MFE (UC Berkeley Haas, 2018) and CMT (Levels I-III, 2020). Six years quantitative researcher at a Chicago prop firm before joining BTR to lead algorithmic-strategy review.

Read our full Testing Methodology.