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How the three exit layers differ
| Layer | What it measures | What it can do |
|---|---|---|
| Position or entry | Price movement and elapsed time associated with an individual entry | Protect or close that entry using stop loss, take profit, trailing protection, or a time limit |
| Basket | The combined position relative to the basket’s average price | Close the whole basket at a target or hard stop; the article says a configured trailing exit replaces the fixed basket take profit |
| Bot equity | Current bot value relative to starting capital, including realized and open P&L and fees, as described in the article | Close positions and stop the bot when an equity threshold or equity trail is reached |
The official guide documents the position-level fields and execution behavior. The basket and equity descriptions and numerical examples are from Moon The Train’s 2026 article; the reviewed official guide does not independently confirm those specific basket defaults or equity examples.
1. Entry-level protection: control one position
QuantDinger’s Strategy API V2 guide lists stop loss, take profit, trailing-stop distance, trailing activation, and a time limit as protections associated with an entry. It clarifies: “Percentage fields are ratios: 0.03 means 3%.” In the guide’s illustrative code example, the values are a 3% stop loss, 8% take profit, 2.5% trailing distance, 2% activation, and a ten-day time limit. Those values demonstrate parameter format; they are not general recommendations.
A trailing activation threshold can delay the trailing rule until price has moved favorably by the configured amount. After activation, the trailing distance governs how far price may retreat before the exit triggers. Check the specific strategy implementation for exact behavior rather than assuming that every bot applies trailing protection identically.
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2. Basket exits: close the averaged position
In the article’s description of the bot templates, basket take profit and hard stop are measured from the basket’s average price rather than from one entry alone. That matters when a strategy adds entries: the basket’s reference price and aggregate exposure can differ from those of any individual position. The article also says that enabling trailing turns off the fixed basket take profit and uses the trailing exit instead. These are descriptions of the templates in that article, not a platform-wide guarantee established by the reviewed official documentation.
3. Bot-equity controls: stop the run
The article describes the bot-equity rule as comparing current bot value with starting capital while accounting for realized P&L, open P&L, and fees. Its reported example settings are a +10% equity target, a −6% equity stop, and a trail that activates at +5% profit and exits after a 3% giveback. Moon The Train reported these values in 2026 as template examples; they may be changed or overridden and are not independent performance statistics or return forecasts.
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These controls operate at a broader scope than per-position or basket exits: the article says they can close positions and stop the bot. The distinction helps explain why a bot-level loss cap may matter even when individual positions have their own stops, particularly when multiple positions contribute to total exposure.
How trigger rules relate to actual fills
QuantDinger’s Strategy API V2 Development Guide distinguishes completed-bar strategy signals from real-time protection. Strategy signals use completed bars, while real-time prices are used for stop loss, take profit, trailing protection, and equity risk. A protection can therefore trigger between strategy bars.
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- In backtests, when price gaps through a threshold, the guide says the fill is at the available bar open; an intrabar touch fills at the trigger price.
- If several protections trigger in one bar, conservative mode prioritizes stop loss, trailing stop, time limit, then take profit.
These documented rules mean a trigger threshold should not automatically be read as the fill price, especially when a bar opens beyond the threshold. Backtest assumptions should match these gap and intrabar-touch semantics when comparing outcomes.
What the example figures can—and cannot—tell you
Moon The Train says the article’s examples draw on default-template behavior and QuantDinger preview calculations, but its author did not run the bots live or backtest them on tick data. The article also notes that defaults can change after the named commit and that users can override them. Its example of a trailing-based win depends on how far price continues after the trail activates. Accordingly, the reported settings and preview arithmetic do not establish profitability, a dependable win rate, or what a live account will earn.
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Checks before using exit rules live
QuantDinger’s live-trading safety guide recommends operational checks in addition to strategy settings:
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- Use a dedicated or low-balance account and grant only the permissions the bot needs.
- Verify the instrument identity and validate the strategy before deployment.
- Have a human review backtest data, costs, slippage, funding, and drawdown.
- Reconcile positions, define explicit exposure and loss limits, and confirm that an operator can stop the bot.
- Monitor runtime state, order status, fills, positions, available balance, and notifications.
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