# Active Trader > Any trader enrolled in the Discentra coaching cohort during the billing period, regardless of whether a call was triggered. ## AI Snippet What is an active trader in Discentra's framework? An active trader is any trader enrolled in the coaching cohort during the billing period, regardless of whether a call was triggered that month. This enrollment-based definition is broader than the industry standard, which typically counts only traders who placed a trade within a given window. The distinction matters because behavioural patterns develop before, between, and after individual trading sessions; the coaching layer monitors continuously, not only when a trader is active in the market. ## What Active Trader is In Discentra's framework, an active trader is any trader enrolled in the coaching cohort during the billing period, regardless of whether a call was triggered that month. This definition is broader than the industry standard, which typically counts only traders who placed a trade within a given window. The distinction is not bookkeeping. It shapes the entire product philosophy. Most broker and prop firm platforms define active trader on activity thresholds: a trade in the last 30 days, a login in the last 14 days, an open position right now. These definitions work for billing infrastructure and for retention dashboards that count revenue-generating users. They do not work for behavioural coaching. The behavioural patterns that drive churn develop in the gaps between sessions, not only during them. Consider a trader who goes quiet for two weeks after a losing session and then re-enters the market with an aggressive position. An activity-based definition would treat the gap as inactivity and the return as a fresh starting state. A behavioural model recognises the gap itself as a signal: avoidance, recovery framing, and re-entry size deviation are the precursors to the next blow-up. Continuous enrollment means the coaching layer is present from the first trade back, not after a re-enrollment process or a manual flag. Enrollment-based monitoring picks up signals that activity-based models cannot see. Session frequency drops below the trader's own baseline. Time-between-trades expands then collapses suddenly. Position sizing on the return trade deviates from the historical average. Stop-loss placement widens or disappears. None of these signals require the trader to be currently active in the market. They require the trader to be in the cohort, with their baseline known and their deviation measurable. Definitions of active trader vary by context. Brokers use activity thresholds for revenue reporting and dashboard counts. Prop firms use them for funded-account tracking. None of these definitions were designed for behavioural intervention; they exist for financial or operational reporting. Treating any of them as a coaching-layer enrollment definition imports the wrong assumptions and creates gaps where the behavioural model should be live. The enrollment-based definition also simplifies commercial terms. Institutions know their cohort the moment they enroll a group of traders. There are no billing disputes over whether a trader "counted" in a given month based on activity thresholds. The trader is in the cohort or they are not. Procurement teams at financial institutions value this clarity: predictable cost structures matter for budget approval, and ambiguous active-user definitions are a known source of friction in SaaS contracts. The re-engagement scenario shows the value gap concretely. A trader inside the cohort who steps away after a loss carries an elevated risk profile when they return. Activity-based platforms reset that profile on re-entry; enrollment-based platforms preserve it. If the returning trader places a trade 1.7x their historical average size within seconds of opening the platform, the coaching layer fires inside the four-minute window. An activity-based system might not register the trader as active until the third or fourth trade. By that point the cortisol cycle has run its course and the account damage is already done. The active trader definition reflects a product philosophy: behavioural coaching works best as a continuous layer, not an on-demand service. Traders who know coaching is available change their behaviour from the start. The psychological safety of knowing someone is paying attention reduces the intensity of tilt episodes before they begin. Coaching, not financial advice. ## Why it matters for institutions The enrollment-based definition is more than a billing convention. It encodes the product thesis that behavioural intervention requires continuous presence. A trader who only sees coaching when they trigger an intervention treats coaching as a punishment. A trader who knows the cohort is monitored continuously treats coaching as a layer of support, which changes the response when a trigger fires. For institutions, the metric that matters is retention rate, not call count. A month with fewer calls is not a month with less value. It may be a month where the coaching layer reduced trigger frequency below the call threshold by changing trader behaviour upstream. Calls are the visible output; the continuous-monitoring presence is the load-bearing input. The active trader count is what scales the value of the coaching layer, not the call volume. Prop firms, brokers, and crypto exchanges that run funded accounts, retail trading platforms, or derivatives desks share the same operational gap: their existing dashboards flag traders only when activity crosses a threshold, and those flags arrive too late. The enrollment-based active-trader model closes that gap because the coaching layer reads behavioural signals continuously, including during quiet periods. The catastrophic-loss session that takes a trader from funded to terminated typically follows an inactive stretch. Activity-based systems are blind during the gap that matters most. The retention economics are sharp. A retained trader compounds in lifetime value across renewal cycles; a churned trader represents a sunk acquisition cost. Across cohorts of meaningful scale, catching a single tilt cascade that would have ended a funded account translates into the difference between a renewed customer and a re-acquisition expense. Enrollment-based coaching is built around the trader's full lifecycle, not the moments when an alarm fires. That continuity is what makes the retention math work. ## Frequently asked questions ### What is an active trader? In general use, an active trader is one who trades frequently rather than buying and holding. In Discentra's coaching model the term is broader: any trader enrolled in the coaching cohort during the billing period, monitored continuously, regardless of whether a call was triggered that month. The behavioural patterns that drive churn develop between sessions, so the coaching layer reads them continuously, not only when a trade fires. ### Is an active trader the same as a day trader or a broker's "Active Trader" platform tier? No. Day trading describes a trading frequency, and several brokers sell a premium "Active Trader" platform tier. Discentra's active trader is a coaching-cohort enrollment definition, not a trading style or a software product. It exists to scope behavioural monitoring and commercial terms, not to describe how often someone trades. ### How is Discentra's active-trader definition different from the industry standard? Most platforms define an active trader on activity thresholds: a trade in the last 30 days, a login in the last 14, an open position now. Those work for billing and revenue dashboards but miss the gaps between sessions where churn behaviour forms. Discentra's enrollment-based definition keeps the coaching layer live from the first trade back, so a trader who goes quiet after a loss and returns with an oversized position is still covered. ### How do active traders relate to trader retention? The active trader count is what scales the value of the coaching layer, not the number of calls placed. A month with fewer calls can be a month where continuous monitoring reduced trigger frequency upstream. Retention rate, not call volume, is the metric that matters, and it compounds across the full enrolled cohort. ## Related terms - [Churn](https://discentra.ai/glossary/churn) - [Trader Retention](https://discentra.ai/glossary/trader-retention) - [Behavioural Trigger](https://discentra.ai/glossary/behavioural-trigger) - [Tilt](https://discentra.ai/glossary/tilt) - [Revenge Trading](https://discentra.ai/glossary/revenge-trading) - [Intervention Window](https://discentra.ai/glossary/intervention-window) ## Further reading - [Discentra for Prop Firms](https://discentra.ai/prop-firms) - [Discentra for Brokers](https://discentra.ai/brokers) - [Discentra for Exchanges](https://discentra.ai/exchanges) - [The Real Cost of Trader Churn](https://discentra.ai/blog/the-real-cost-of-trader-churn) - [Five Metrics That Predict Churn](https://discentra.ai/blog/five-metrics-that-predict-churn) - [How Discentra Works](https://discentra.ai/how-it-works) --- This is a Markdown mirror of [https://discentra.ai/glossary/active-trader](https://discentra.ai/glossary/active-trader). Generated for LLM citation. © Discentra Ltd. Coaching, not financial advice.