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A margin guard can appear to enforce a limit while approving entries that exceed it. In Sam Hartley’s account, four defects made a Python trading bot’s guard read zero: missing state defaulted to zero, some values used notional instead of margin, position-level fields did not reflect cross-margin usage in that account, and the guard was not refreshed inside the entry loop. Hartley’s report is a specific incident, not evidence that every exchange or account exposes margin the same way.
What the guard was supposed to do
Hartley describes an hourly funding-rate scan that could open as many as three positions. The intended rule capped total margin at 50% of account equity. Yet the guard did not reliably measure margin in use at the moment a new position was considered.
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The distinction matters: a threshold only protects an account if the value being compared is known, expressed in compatible units, relevant to the account’s margin mode, and current for the action about to be taken.
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1. Missing state was treated as a safe value
The code used state.get("margin_used") or 0.0. That expression maps both a genuine zero and a missing value to 0.0. The guard therefore could not distinguish “no margin is in use” from “the program does not know how much is in use.”
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For a safety control, unknown usage should not silently authorize a new entry. Make the missing-data policy explicit: if the required value is absent or invalid, stop entry and surface the problem for diagnosis. This is fail-closed behavior; defaulting unknown exposure to zero is fail-open.
2. Notional and margin were mixed
Some code paths reportedly stored position notional—position size multiplied by leverage—in margin_used, then compared that value with a cap defined in account margin. Notional and margin are different quantities. A unit mismatch can still produce a plausible-looking number, so the code may run without an obvious exception while making the comparison meaningless.
Choose one definition for the guard and use it consistently on both sides of the comparison. Name variables for what they measure, document the units, and avoid reusing a field for a different quantity on another code path.
3. The remote fields did not answer the needed question in this account
Hartley reports that, in the account’s cross-margin mode, collateral was account-level while the position-level position_margin and order_margin fields were zero for open positions. In a read-only check, three positions showed zero in both fields, while cross initial margin was $460.97. Those figures describe Hartley’s account check; the report does not identify an exchange or establish how other exchanges, APIs, or account modes behave.
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A field name is not proof that a value measures the quantity your risk rule needs. Verify the current API documentation and account configuration for the specific system in use. If the field is not meaningful for that account mode, do not treat its zero as evidence of zero exposure.
4. Usage was checked before, not during, the entry loop
The guard reportedly ran once during synchronization before candidate processing. The loop could then open multiple positions without refreshing usage. A snapshot taken before the first action becomes stale as soon as the bot changes the account by opening a position.
Re-read or recalculate mutable usage immediately before each constrained action. Each candidate should be evaluated against the state left by entries already made in the same scan, not against the original snapshot.
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Hartley reports that five identical entry signals opened five positions under the old behavior. The run used $933.89 of margin, described as 83.5% of the account against a 50% cap. In the revised run, the guard saw $0, $186.78, $373.56, and $560.34; it blocked the fourth entry. The three resulting positions reportedly used $560.34, or 50.1% against the 50% cap.
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These are the author’s reported run figures, not independently verified measurements or general performance benchmarks. They illustrate why checking a value once, or trusting a field that does not represent the intended quantity, can defeat a numerical limit.
How the reported fix changed the calculation
Hartley describes calculating usage from active positions as capital_at_entry / leverage, with a lower bound on leverage, then recalculating from the live position list inside the candidate loop before each new entry. Exchange-provided figures remained in diagnostics rather than serving as the guard’s controlling value.
The general design is to derive the guard’s input from a clearly defined source of held positions, keep the calculation in the same margin unit as the threshold, and refresh it at the point of decision. This approach still depends on the position data and formula accurately representing the account’s exposure; it does not make account-mode or data-quality checks unnecessary.
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The described revision checks current usage against the cap but does not add the next candidate’s expected margin before approving it. As a result, it can approve an entry that takes usage past the limit. Hartley reports three 16.7% slots reaching 50.1% against a 50% cap; a 30% slot could create a 10-percentage-point overshoot.
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To enforce a strict cap, evaluate projected usage before entry: current usage plus the candidate’s expected margin must be at or below the limit. The projected amount should use the same definition and units as current usage. If the estimate is unavailable or cannot be trusted, the policy should say whether to block the entry rather than imply that the cap is guaranteed.
Make the test sensitive to the fix
A test that independently recreates the guard formula can pass even if the production code remains unchanged. Hartley describes replacing that style with a test that extracts the guard block from source, runs patched and unpatched versions against the same scenarios, and asserts that their outputs differ.
In the reported missing-value scenario, the unpatched behavior stopped after two positions, while the patched version opened three and then blocked. The author’s control assertion that patched and unpatched outputs are not identical is intended to catch a test that cannot detect whether the change affected behavior. This is the author’s test description, not a separately executed verification.
Tests should also cover the decision’s important boundaries: absent data, a real zero, incompatible units, account-mode field behavior, and a changing position list across multiple candidates. For a strict cap, include a candidate whose expected margin would push projected usage over the threshold.
A practical review checklist
- Unknown values: Does missing or invalid usage block new entries instead of becoming zero?
- Units: Are the measured value and threshold both margin, rather than a mixture of margin and notional?
- Meaning: Does the chosen field or calculation represent the needed quantity for this account mode?
- Freshness: Is usage recalculated after each action and immediately before the next constrained action?
- Next-entry impact: Does approval include the candidate’s expected margin when the cap must be strict?
- Test sensitivity: Would a test fail if the production change were removed?
For the account-specific report and implementation details, see Sam Hartley’s original article.
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