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When a trading bot reads margin usage as zero, that value may mean “unknown,” “measured in the wrong units,” or “not represented by this account-mode field”—not that the account has no margin in use. Sam Hartley’s postmortem describes four interacting defects in a Python bot and a practical fix: derive usage from held positions and re-read it immediately before each new entry. The reported figures are Hartley’s account-specific results, not independently verified benchmarks or universal exchange behavior.
What failed in Hartley’s margin guard?
Hartley describes an hourly funding-rate scan that could open as many as three positions, with a rule intended to keep total margin below 50% of account equity. The guard read zero and allowed entries because four separate problems obscured the real exposure or let the bot act on stale information.
1. Missing state became a safe-looking zero
The code used state.get("margin_used") or 0.0. That expression maps both a missing value and a genuine numeric zero to 0.0. The guard therefore could not distinguish “usage is unknown” from “usage is confirmed to be zero.” For a safety limit, that is fail-open behavior: uncertainty permits the action instead of stopping it.
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2. Notional was compared with a margin limit
Some code paths reportedly stored position notional—position size multiplied by leverage—in margin_used, then compared it with a limit expressed as account margin. Notional and margin are different quantities. Because both can look like ordinary dollar figures, this kind of unit mismatch can produce plausible-looking results rather than an obvious error.
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3. The remote fields did not reflect cross-margin usage in this account
Hartley says that, in the account’s cross-margin mode, collateral was tracked at the account level while the position-level position_margin and order_margin fields were zero for open positions. In a read-only check, Hartley reports zero in both fields across three positions alongside $460.97 in cross initial margin. This describes Hartley’s account and report only; it does not establish how another exchange, API, or account configuration behaves.
4. The guard was not refreshed during the entry loop
The check reportedly ran once during a sync before candidate processing. The loop could then open multiple positions without recalculating usage. Even a correct snapshot becomes stale as soon as the bot changes the account, so checking only before a sequence of actions does not enforce a changing limit across that sequence.
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Why the guard needs both a reliable value and a fresh one
A numerical threshold is useful only if the value being compared is present, expressed in compatible units, and meaningful for the account mode. It also needs to describe the account at the moment the bot is about to act. A zero returned by a field is not self-explanatory: the code must know whether it means verified zero usage or a field that does not represent the quantity the guard needs.
- Presence: Missing or unparseable usage should have an explicit policy. For an entry-safety check, a conservative policy is to block new entries until usage can be established.
- Units: Keep the measured quantity and threshold aligned. Do not compare position notional with a margin cap.
- Semantics: Confirm what a remote field means in the specific account mode and configuration, rather than inferring account-wide usage from a position-level value.
- Freshness: Recalculate mutable usage immediately before every constrained action.
- Next-action exposure: Decide whether the proposed position’s expected margin must be included before approving it.
What changed in the described fix?
Hartley says the revised calculation derives usage from active positions as capital_at_entry / leverage, with a lower bound on leverage, and recalculates that figure from the live position list inside the candidate loop before each new entry. Exchange-reported figures remain diagnostic rather than serving as the guard’s primary input in the described change.
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The important structural change is the location of the check: it runs where an entry is about to happen, not just once before the loop. That lets the next check see positions opened earlier in the same scan. The underlying calculation still depends on the position data and formula matching the account’s actual margin model; the post does not establish that the formula applies to every trading system or account mode.
What did the reported runs show?
Hartley reports that five identical entry signals in one scan opened five positions under the old behavior, using $933.89 of margin—described as 83.5% of the account against a 50% cap. With the revised behavior, the reported checks saw $0, $186.78, $373.56, and $560.34; the bot then blocked the fourth entry. The resulting three positions reportedly used $560.34, described as 50.1% against the 50% cap. These are figures from Hartley’s account and runs, not independently validated measurements or general benchmarks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can a test show that the fix matters?
A test can pass while failing to detect the defect if it simply recreates the guard’s intended formula rather than exercising the code that actually decides whether to enter. Hartley describes replacing such a test with one that extracts the guard block from the source and runs patched and unpatched versions against the same scenarios, then asserts that their outputs differ.
For the reported missing-value scenario, Hartley says the unpatched behavior stopped after two positions, while the patched behavior opened three and then blocked; the control assertion checks that patched and unpatched outputs are not identical. This is the author’s test description, not a test independently executed for this article. The general lesson is to include a control that would fail if the patch made no observable difference in the scenario designed to expose the bug.
What limitation remains in the revised guard?
Hartley notes that the revised check compares current usage with the cap but does not add the proposed position’s expected margin before approving entry. It can therefore overshoot the threshold by as much as one entry’s margin. In the reported example, three slots of about 16.7% each produce 50.1% against a 50% cap; a single 30% slot could produce a 10-percentage-point overshoot if approved when current usage is already at the cap.
To enforce a hard ceiling rather than a limit on current usage, the approval condition needs to account for the candidate as well as existing positions—for example, by comparing current usage plus the candidate’s expected margin with the cap. That requires a defensible estimate of the candidate’s margin and a consistent unit for both terms.
How to verify a margin field before relying on it
- Identify the exact account mode and configuration in use, including whether margin is isolated or cross-account.
- Check the exchange’s current documentation for the field’s definition and scope; confirm whether it reports position-level or account-level usage.
- Compare the field with a read-only account view and known open positions in that same mode. Treat a mismatch as unresolved, not as evidence of zero.
- Trace every value written to the guard input and confirm its units, source, and behavior when missing or invalid.
- Test the entry path with several sequential candidates, verifying that the guard observes the updated position list before each decision and applies the intended policy to unknown usage.
- If the limit is a strict maximum, include the next candidate’s expected margin in the approval calculation and test a candidate large enough to cross the cap.
Hartley’s account of the incident and implementation is available in the original postmortem.
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