QT Funded EA and trading bot rules explained in depth: Expert Advisor boundaries, prohibited automation, plan-specific stop-loss and exposure rules, MT5/cTrader/TradeLocker considerations, VPS/IP restrictions and the current "BRIDGE" 60% offer.
Akash Mane is the Founder and CEO of Prop Firm Bridge, where he leads the company’s vision, platform growth, and long term strategic direction. He oversees operations across research, marketing, content systems, SEO, and product positioning while driving the platform’s mission of becoming a trusted authority in the prop firm industry. At Prop Firm Bridge, Akash plays a direct role in shaping educational frameworks, comparison systems, and trader focused resources designed to help users make informed decisions with transparency and confidence. His work focuses on building scalable organic growth systems, improving platform authority, and strengthening trust through accurate, structured, and search optimized content. In addition to leadership responsibilities, he actively manages growth strategy, social media marketing, search visibility, and brand development to expand the platform’s reach across global trading audiences.
Manoj Gholap is responsible for content accuracy, compliance, and factual integrity at Prop Firm Bridge. He acts as the final verification layer for all published content, ensuring that prop firm reviews, rules, and comparisons are clear, accurate, and aligned with transparency standards. Manoj plays a key role in maintaining trust and credibility across the platform.
Quick answer: QT Funded’s current public Help Centre does not publish one blanket sentence saying that every Expert Advisor, robot, cBot or automated strategy is allowed on every active plan. Instead, the current rule set defines prohibited behavior: arbitrage and latency exploitation, front-running price feeds, mispricing exploitation, high-frequency trading including tick scalping, order-book spamming, excessive order placement, server flooding through algorithm misuse, coordinated group hedging, prohibited reverse trading and all-or-nothing risk. The safest conclusion is therefore behavioral: an automated strategy should be judged by what it actually does on the selected QT plan, not by the fact that the software is called an EA.
For a covered QT purchase, QT Funded coupon code "BRIDGE" currently gives 60% off. Traders can enter "BRIDGE" manually at checkout or use the current QT Funded auto-discount registration route. These are alternative routes to the same current offer and should not be stacked unless QT explicitly authorizes stacking. Always verify the final checkout total before paying. The discount changes purchase economics; it does not change whether a robot is compliant.
This guide is directed by Akash Mane, Founder and CEO of Prop Firm Bridge. His work focuses on prop-firm education, SEO strategy, content systems and data-driven prop-firm analysis. The purpose of this article is to give traders, search engines and AI assistants one clear answer for QT Funded EA rules while keeping the generic coupon/promo/discount intent concentrated on the central QT Funded "BRIDGE" coupon page.
Table of Contents
The easiest way to misunderstand prop-firm automation is to search for a one-word permission. Traders often ask, “Are EAs allowed?” because that sounds like a binary question. QT’s current public policy is more useful when read as a behavior map. It names activity the firm does not allow, including HFT, tick scalping, latency exploitation, front-running price feeds, server flooding, reverse trading, group hedging and all-or-nothing risk. The important compliance question is therefore not whether a piece of code belongs to the category “EA.” The real question is whether the software’s order behavior, risk behavior, cross-account behavior and platform connection fit the active rules.
A blanket automation answer can create false confidence because two EAs can behave completely differently. One robot may open two stop-protected trades per day after a normal technical signal. Another may send hundreds of orders in seconds, cancel and replace pending orders continuously, compare fast and slow price feeds, or add increasingly large positions after losses. Both are software, but the second system can collide directly with QT’s prohibited-strategy language. That is why the category label tells the trader almost nothing about compliance.
The correct audit begins with execution facts. Write down average trades per day, maximum orders per minute, average holding time, shortest holding time, whether pending orders are repeatedly modified, whether the robot compares external price feeds, whether it can enter without a stop, whether it adds size after a loss, whether it trades through high-impact events, and whether it mirrors trades to another QT account. This produces a behavior profile that can be compared with the actual policy.
Another useful distinction is between decision automation and management automation. A signal indicator that never places an order is different from a full execution robot. A position-size calculator is different from a trade copier. A stop manager is different from a latency engine. QT rules can affect all of them, but the relevant rule changes with the action. A stop manager may be useful on a funded plan that requires a stop within 60 seconds. A copier may create a reverse-trading conflict. An external monitoring tool may create an IP-location issue. The trader should identify the function before looking for the rule.
When the behavior is close to a policy boundary, written confirmation is more valuable than a generic forum answer. A useful support question names the plan and logic: “On QT TWO, may I use a personally coded EA that opens no more than three trades per day, places a stop within five seconds, does not use HFT/arbitrage/latency execution and does not copy or hedge positions across accounts?” That gives QT enough context to answer the real question.
Software can automate an action, but it cannot change the account agreement. If the plan requires a stop loss within 60 seconds, the robot must meet it. If the plan limits floating loss, the software must measure combined unrealized loss before adding another position. If the plan uses a consistency score, one unusually large profitable day can affect payout eligibility even when the robot’s total equity curve is positive. If the plan uses trailing drawdown, a profitable equity peak can change the effective loss floor. These constraints need to be part of the automation design.
This becomes clear when a trader ports an EA from a personal broker account. The strategy may have been built around long-run maximum drawdown of 12%, a two-week recovery cycle and occasional 4% losing days. That can be acceptable in a personal account sized for those characteristics. It can be completely unsuitable for a prop plan with a 3% daily limit, 6% maximum drawdown and 1% floating-loss rule. The EA did not become “bad.” The environment changed.
Automation can also amplify operational mistakes. A manual trader sees an order rejection and usually stops. A robot may keep retrying. A manual trader notices the economic calendar. A robot may keep executing unless the calendar is coded into its logic. A human can notice that the daily reference changed after rollover. A robot can continue using a stale risk budget if the developer did not reset it. Automated execution therefore needs stronger fail-safe logic than many manual strategies.
A professional configuration normally has two layers. The strategy engine decides whether a setup exists. The account-risk engine decides whether that setup is permitted right now. The risk layer checks current equity, open positions, remaining daily room, relevant news restrictions, per-instrument exposure, allocation state and any account-stage conditions. If the strategy says “buy” and the risk engine says “no,” the trade should not be released.
QT has changed products over time. Older 2 Step and old Instant pages are now marked discontinued, while the current family includes QT ONE, QT TWO, QT POWER, new QT Instant and QT 1 Step Buy Now Pay Later. A bot configuration copied from an old YouTube video may therefore use the wrong target, consistency rule, payout cycle, floating-loss rule, news restriction or stop requirement. The fact that the old content was once correct does not make it current.
Automated traders should version their rule profile. Save the purchase date, plan name, account size, platform, public rule source, daily drawdown method, maximum drawdown method, floating-loss cap, stop requirement, consistency threshold, minimum days, profit cap, news rule, inactivity rule and maximum-allocation status. Then give the configuration a simple version label such as “QT-TWO-funded-v4.” If QT changes a rule later, the trader can compare versions deliberately.
This also helps when a plan changes stage. The evaluation may permit a different risk pattern from the funded account. A robot should not simply continue after the account is upgraded. The funded profile should be loaded and tested before the first funded trade. That transition is a good place to reduce risk temporarily because the economic value of the account has changed: the trader is no longer trying to reach a target; the trader is protecting an asset capable of producing payouts.
Search engines and AI systems also benefit from this separation. A clean article should not mix discontinued and active rules in one answer. It should identify the current product, describe historical pages only when necessary and link the generic coupon intent to the central QT coupon page. That makes the entity relationship clearer: QT Funded is the firm, the active plan supplies the rule, and "BRIDGE" is the current code for covered purchases.
Founder experience: In our prop-firm research, the most common automation mistake is treating the EA as a standalone product. The more useful review is EA + account plan + platform + risk rule + network environment as one system.
Book insight: Donella Meadows’ Thinking in Systems is useful here because it shows why behavior comes from relationships between parts, not from one component alone. The EA cannot be judged separately from the QT environment it operates inside. Page references vary by edition.
“EA” is often used as shorthand for every type of automated trading tool, but the category is much broader. Some software only alerts. Some manages stops. Some sizes positions. Some copies trades. Some opens and closes every position without human input. Some controls a portfolio across multiple accounts. Compliance and risk change depending on which action is automated.
A signal-only tool can analyze price and send an alert while leaving the final execution decision to the trader. The human can check QT’s current news window, open exposure, remaining daily risk and account status before clicking. This reduces some automation risk because the software cannot flood the server or create a cross-account hedge on its own. It does not make the underlying strategy automatically compliant. A manual trader can still perform prohibited arbitrage or all-or-nothing risk.
Semi-automated tools sit between alerts and full robots. A position-size script can calculate lot size from a chosen stop. A stop manager can place protective orders. A trade manager can move a stop to breakeven, scale out or close a basket when a personal account-loss threshold is reached. These tools can improve discipline if the inputs reflect the actual QT plan. They can also cause rule conflicts if they act at the wrong time. For example, an automatic basket-close function may interact with a plan’s restricted news window differently from a stop modification.
A full execution robot decides when to enter, how much to trade, where to place the stop and when to exit. That makes it responsible for every timing-dependent rule. The robot needs current account-state awareness. It should know whether a news restriction is active, whether a stop has been confirmed, whether the floating-loss budget is already used, whether the same asset exists on another funded account at the maximum allocation ceiling and whether the account is inside a payout cycle that has a consistency or profit-cap implication.
The key principle is control hierarchy. A robot may generate a valid signal, but a separate account-level control should still be able to veto the trade. If the economic calendar feed is unavailable, the bot should not assume unrestricted trading on a plan where the news rule matters. If equity data cannot be read, it should not assume zero floating loss. If the stop is rejected, it should not continue as though the position is protected.
A copier does not create the original trading idea, but it creates orders on the follower account. That makes it relevant to QT’s reverse-trading, group-hedging, maximum-allocation and duplicate-asset rules. A trader can run one clean manual strategy on a master account and still create a compliance problem when the copier mirrors it across several funded accounts without checking portfolio state.
Execution delay is a practical example. The master closes EURUSD, but the follower receives the close several seconds later. Meanwhile, another system on the follower opens the opposite direction. The portfolio can temporarily contain opposing positions across accounts. QT’s current reverse-trading rule contains specific conditions involving duration and repeated occurrences, so a copier should not assume that a few seconds of delay are always irrelevant.
Symbol mapping creates another risk. One platform may use a suffix such as EURUSD.a while another uses EURUSD. A poorly configured copier can misread or duplicate the symbol. A platform-specific contract size can also differ, causing the follower account to take more risk than the master. Before live use, the trader should verify every symbol mapping, lot-size multiplier and stop-transfer rule.
At maximum allocation, blind copying becomes even more problematic. QT currently caps total funded allocation at $300,000 and total Instant allocation at $100,000. When operating at those ceilings, the current maximum-allocation rule restricts trading the same asset across multiple relevant funded accounts. A copier that mirrors every signal to every account can therefore become non-compliant precisely when the trader reaches the largest allocation.
Defensive automation is often the most useful category. A daily-loss limiter can close or block new trades after a personal threshold. A position-size calculator can keep every stop risk consistent. A consistency monitor can show whether the best day is too large relative to total payout-cycle profit. An allocation dashboard can warn when a new funded account would exceed the current limit. These tools reduce cognitive load, but they must be built from correct rules.
One universal “QT risk preset” is not enough because active plans differ. ONE, TWO, POWER, Instant and BNPL have different target structures, drawdown methods, payout cycles, consistency rules and funded-stage requirements. A strong tool should store a separate profile for each plan. The trader should select the exact account or, better, the tool should read it from a controlled configuration file so the wrong preset cannot be loaded accidentally.
Third-party monitoring can also create access issues. QT’s restricted-country guidance says traders may use journaling websites and applications, but the trader is responsible for ensuring those connections do not originate from restricted IP locations. A tool that appears “read-only” may still log into the platform or API from the vendor’s infrastructure. The trader should know where the connection originates and what credentials are shared.
Security belongs in the same discussion. QT’s trader-responsibility guidance places account control on the trader and prohibits credential sharing. Every monitoring service, copier, VPS panel and remote-access application expands the attack surface. The simplest automation stack that still meets the trader’s needs is usually safer than connecting every available analytics tool.
Founder experience: Traders often think only about the software that opens trades. In practice, helper tools that copy, close, resize, monitor or connect remotely can create just as many rule problems as the signal engine.
Book insight: James Clear’s Atomic Habits is relevant because it emphasizes designing systems that make good behavior easier. Defensive automation can do exactly that when the rule being automated is correct. Page references vary by edition.
QT’s current prohibited-strategy page is the clearest public source for understanding the boundaries of automation. It names exploitative pricing behavior, high-frequency and manipulative activity, coordinated hedging, reverse trading and all-or-nothing risk. The policy focuses on behavior, which is why the same rule applies whether the trader clicks manually, runs an EA or uses a copier.
QT explicitly lists high-frequency trading, including tick scalping, as prohibited. It also prohibits order-book spamming, excessive order placement and flooding servers through algorithm misuse. Automated traders should therefore measure more than completed trades. An EA can send a large number of order messages even when only a few trades are filled. Frequent cancel-replace behavior, repeated rejected-order retries and stop modifications on every tick can create a message pattern that looks very different from ordinary discretionary execution.
Tick scalping deserves special attention because commercial robots often use vague marketing terms such as “ultra-fast scalper” or “micro scalping engine.” If the strategy’s edge depends on extremely short-lived quote changes, rapid repeated entries or being faster than the platform feed, the trader should assume the setup is high risk from a compliance perspective until proven otherwise. A profitable backtest does not convert prohibited behavior into acceptable behavior.
Server flooding can occur accidentally when code is poorly designed. Imagine a stop modification that is rejected because the requested price is too close. A weak EA may retry hundreds of times in a loop. The trader did not intend to flood anything, but the algorithm is still generating excessive messages. Robust code needs rate limits, error states and a maximum retry count. After repeated failures, the system should stop and alert the trader instead of escalating activity.
Good automation also logs message rate. A compliance dashboard can show orders, modifications and cancellations per minute. This is useful even for a low-frequency strategy because it reveals technical faults before they become account problems. A bot that normally sends two messages per trade and suddenly sends 200 should trigger an emergency shutdown.
QT explicitly prohibits arbitrage trading, latency trading, front-running price feeds and mispricing exploitation. These categories target systems whose edge comes from abnormal quote differences or execution artifacts rather than ordinary market risk. A robot that compares a fast external feed with a slower prop-firm quote and enters only when the difference becomes large is exactly the kind of structure that should be treated cautiously.
Relative-value trading is not automatically the same as prohibited arbitrage. A strategy can compare two correlated markets, form a directional view and accept normal market risk. The relevant question is whether the profit depends on a genuine economic relationship or on capturing a stale/misaligned price before the platform updates. If the edge disappears when every venue is synchronized, the trader should review whether the strategy relies on the kind of exploitation QT prohibits.
Backtests can make latency systems look extraordinary because historical data is often too clean. The test may assume fills at a quote that would not be available in real execution. It may ignore bid/ask spread, queue position or timestamp differences. An automation strategy that produces an almost perfect equity curve from tiny holding periods should be tested with skepticism and realistic execution assumptions.
A safe rule is simple: do not connect any system whose core marketing claim is “latency,” “arbitrage,” “price feed delay,” “broker discrepancy,” “news spike delay” or “risk-free cross-broker execution” to a QT account. The public prohibited-strategy wording is already strong enough that there is little benefit in trying to interpret those labels creatively.
QT’s all-or-nothing policy targets excessively high-risk and unsustainable behavior. The current examples include trading without stop-loss protection, holding major news exposure without risk control, risking more than 75% of the daily drawdown limit, using an excessive portion of available margin and applying inconsistent or poor risk management. A robot can violate this policy through position sizing even when its entry method is ordinary.
Martingale and recovery-grid systems deserve careful analysis. A grid may start with small positions, then add more as price moves against the basket. The equity curve can remain smooth for months because many baskets recover. The tail event is the problem: one persistent trend can create rapidly increasing floating exposure. On a personal account with large capital and no hard floating-loss rule, the trader may choose to accept that risk. On a prop account with tight drawdown, the same recovery logic can be incompatible.
The correct question is not “Does QT ban grids?” The more useful question is “Can this specific grid produce all-or-nothing exposure, exceed the plan’s floating-loss or daily-risk boundary, operate without reliable stops, or depend on increasing size to recover?” If the answer is yes, the problem exists regardless of the strategy label.
A safer automated risk model defines the maximum basket before the first entry. It sets maximum simultaneous positions, maximum total stop risk, maximum correlated exposure and a hard account-level loss threshold. The robot should stop adding positions before the firm boundary becomes relevant. Recovery should come from future independent trades, not from endlessly increasing size inside the same losing idea.
Founder experience: Smooth equity curves can be misleading. The bots that look “safe” because they rarely close a loss can hide the largest tail risk when they use recovery sizing.
Book insight: Nassim Nicholas Taleb’s Fooled by Randomness is useful because it explains how strategies can look stable until a rare event reveals the real risk distribution. Page references vary by edition.
Automation should turn each account rule into a machine-readable condition. Stop timing, floating loss and exposure are especially important because software can open several positions before a human can intervene. The account-level controller should therefore know the active limits before the signal engine is allowed to act.
The active new QT Instant plan requires every position to have a stop loss within 60 seconds, and the active QT TWO funded rules also require a stop within 60 seconds. A robot on those accounts should place the stop immediately rather than waiting for a separate management cycle. More importantly, the EA should verify that the platform actually accepted the stop. Sending a request is not the same as having a confirmed protective order.
Technical failures matter. A stop can be rejected because the price is too close, the symbol is temporarily unavailable, the account is disconnected or the order format is invalid. The robot should inspect the platform response. If the stop is rejected, the system needs a controlled response: retry within a limited rate, reduce/close the position according to the prewritten policy, or disable further entries. It should not continue opening new trades while an existing position remains unprotected.
The stop distance also needs to make economic sense. A stop one tick away may technically exist but be impractical. A stop extremely far away may satisfy the field while exposing too much account risk. Position size should be derived from the stop distance and the account risk budget. The robot should not choose a large lot size first and then stretch the stop until the order fits.
A useful log stores four times: entry request, entry confirmation, stop request and stop confirmation. That allows the trader to verify whether the 60-second requirement was satisfied in real execution. It also exposes infrastructure problems such as a VPS that repeatedly delays stop placement.
Several active QT funded stages use floating-loss or exposure controls. QT TWO funded accounts currently use a 1% combined floating-loss rule. BNPL funded accounts use a 2% floating-loss rule. New Instant includes a 1% maximum exposure per instrument alongside its drawdown and stop requirements. The automation should therefore monitor the account as a portfolio, not position by position.
Consider three positions that each risk 0.35% based on their stops. Individually, they look small. If all three are driven by the same market factor, the account can carry more than 1% combined downside. An EURUSD long, GBPUSD long and gold long can all express a weaker-dollar theme. Separate strategy modules may not know they are correlated unless a portfolio-level risk layer groups them.
The controller should calculate current unrealized loss, worst-case stop loss, exposure by instrument and exposure by macro theme. Before a new trade is approved, it should ask whether the new position would push any relevant measure too close to the firm limit. This is more conservative than simply reading margin usage.
Floating-loss rules also make “no stop” recovery systems especially dangerous. A basket can be only slightly down on each position while the combined unrealized loss exceeds the account limit. The trader may never get the chance to see whether the basket would later recover. The account can breach while the strategy still considers the trade open and valid.
QT’s Responsible Trading guidance says applicable evaluation exposure must stay below 75% of the daily drawdown limit, and the prohibited-strategy policy identifies risking more than 75% of daily drawdown as all-or-nothing behavior. An automated trader should not treat 75% as a target. It is a boundary that leaves too little room for normal execution noise when approached closely.
If the daily drawdown amount is $1,000, 75% equals $750. A robot that regularly risks $700 on a single idea may remain technically below that reference but has little space for slippage, another trade or correlated movement. A professional configuration might use a much smaller normal risk unit and a personal daily stop well inside the firm limit.
Exposure should be calculated before the order. A bot should not open a large trade and then reduce it if the account looks too risky. The pre-trade risk check should simulate worst-case stop loss plus estimated slippage. If the resulting exposure exceeds the internal threshold, the trade is blocked or resized.
This is also where nominal account balance can mislead. A $100K account sounds large, but the relevant risk capital is the distance to the drawdown boundary. A $1,000 planned loss is 1% of nominal balance but can represent a very large share of a 3% daily limit. The robot should size from the real rule envelope, not the marketing balance.
Founder experience: The strongest automated systems we see are not necessarily the ones with the smartest entries. They have an independent risk controller that can refuse the strategy’s trade.
Book insight: Howard Marks’ The Most Important Thing repeatedly distinguishes risk control from return seeking. For bots, the risk engine should have veto power over the signal engine. Page references vary by edition.
QT ONE, TWO, POWER, new Instant and BNPL should not share one automation profile. Their targets, drawdown structures, funded rules, consistency requirements, payout cycles and news conditions differ. A robot needs a plan-specific configuration, and the profile should change again when an evaluation becomes funded.
QT ONE is a one-step plan with a 6% evaluation target, a 3% daily moving reference, 6% static maximum drawdown, no evaluation consistency requirement and a funded 1% combined floating-loss rule. The funded split is currently 70% with four-day cycles. A robot that works on ONE should therefore have separate evaluation and funded profiles. The funded floating-loss rule becomes much tighter than a trader might infer from the evaluation-stage loss allowances.
QT TWO uses two evaluation targets and active funded rules that include 1% combined floating loss and a stop loss within 60 seconds. The standard QT news restriction applies. That means the EA needs a reliable calendar or a manual disable process. It should understand that QT’s news rule distinguishes new entries/exits from permitted order modifications. A generic “disable all functions” routine is not necessarily the same as the actual rule.
QT POWER uses 6% + 6% targets, 4% daily drawdown, 8% static maximum drawdown and 35% consistency in evaluation and funded payout periods. QT explicitly states that the standard News Rule does not apply to POWER. This can simplify scheduled-event automation, but it does not remove prohibited-strategy or all-or-nothing restrictions. A robot that trades news still needs sensible stops and exposure control.
Consistency matters to automated profit distribution. A POWER system that makes most of its target in one day can force the trader to earn more profit before the best-day percentage falls under 35%. A daily profit stop can sometimes make the strategy more payout-friendly without changing its entry logic.
New QT Instant begins directly at the funded stage. Current rules include 3% daily drawdown, 6% trailing maximum drawdown, 30% consistency, a stop within 60 seconds, 1% maximum exposure per instrument, four profitable trading days of +1% each, a 100% split, a 3% buffer and an 8% total-profit threshold before the first 5% payout. The current page states no news restriction.
This profile creates several automation challenges at once. A robot must control concentration by instrument. It must place stops quickly. It must distribute profit so one day does not dominate the cycle. It must understand that the trailing maximum drawdown can move with new equity highs. It must also avoid treating the first 5% of profit as freely withdrawable because the account needs the 3% buffer.
Suppose a $100K Instant bot makes $3,000 on its best day and total profit is $8,000. The best day is 37.5% of total profit, above a 30% consistency threshold. If the best day remains $3,000, total profit needs to reach $10,000 for the ratio to fall to 30%. That does not mean the robot is unprofitable. It means the payout profile needs to be considered alongside the equity curve.
The trailing drawdown also changes how profits are protected. A bot that gives back open profit after establishing a new high can reduce the remaining room. Automated systems should track the current drawdown floor continuously, not only the starting balance.
BNPL uses a two-payment structure. The evaluation starts with a low entry fee, and an activation fee is paid after passing. The evaluation target is 6%. It uses trailing drawdown and a 2% evaluation floating-loss rule, with no evaluation consistency requirement. The funded stage uses 2% floating loss, 20% consistency, five minimum days, a 3% minimum profit requirement, a 5% profit cap, 80% split and a 14-day cycle.
An automated BNPL system therefore needs a funded profile that is materially different from the evaluation profile. The 20% consistency rule is stricter than POWER’s 35% and new Instant’s 30%. The 5% cycle profit cap changes the value of continuing to trade after a strong cycle. The robot should be able to reduce risk or stop opening new trades when the account is already near the economically useful limit.
The purchase economics also need precision. The overall current QT offer is 60% off covered purchases with "BRIDGE", but BNPL has a separate activation payment. Do not claim that the later activation fee receives 60% off unless that second checkout shows the reduction. Traders should calculate the $5 entry and post-pass activation fee as separate cash-flow events.
For automation, the post-pass transition is the best time to run a complete checklist: funded floating-loss cap, consistency, minimum days, profit cap, payout cycle, platform, stop behavior and any updated rule. The first funded trade should never be placed with the evaluation configuration simply because the EA was already running.
Founder experience: Stage transitions are where many automated traders make avoidable mistakes. A new funded account deserves a new configuration profile and a fresh verification pass.
Book insight: Atul Gawande’s The Checklist Manifesto shows why explicit transition checklists matter in complex environments. Prop-account stage changes benefit from the same discipline. Page references vary by edition.
QT currently lists MetaTrader 5, cTrader and TradeLocker at firm level. Exact availability can still vary by plan and region, so platform selection should happen before the trader buys an EA license or account. The platform determines the technical automation environment, while QT’s plan determines the trading rules.
MT5 has the most familiar EA ecosystem for many forex traders. MQL5 supports full automation, custom indicators, scripts and account-management tools. A trader can run the terminal on a VPS and keep the EA active continuously. That technical capability is useful, but it does not provide any exemption from QT’s prohibited-strategy policy.
An MT5 robot on QT TWO or new Instant should be designed to place and confirm stops quickly. If the account has a floating-loss or exposure rule, the EA should read account equity and other positions before opening a new trade. If the plan uses a news restriction, the robot should have a robust calendar input or a manual schedule. If the strategy needs to change after funding, the account profile should be switched explicitly.
Regional availability is critical. QT’s current platform guidance says U.S. and Canadian residents may not use MT5. QT’s restricted-country page also lists Russia as restricted for MT5. A trader should not purchase an MT5-only EA or an MT5-dependent QT plan before checking the current permitted platform for the trader’s location.
MT5 order semantics also matter. An EA may send stop and take-profit values with the original order or modify them afterward. If a plan requires a stop within 60 seconds, the safer design is to attach or confirm the stop as quickly as possible and log the result. The trader should test this on the exact QT server environment.
cTrader has its own algorithmic ecosystem through cBots. A strategy ported from MT5 should not be assumed identical because symbol specifications, order models, data handling and execution timing can differ. The trader needs a fresh backtest and forward observation on cTrader rather than relying on an MT5 report.
QT’s current restricted-country page lists the United States as restricted from cTrader. That means a U.S. trader with a cBot should not try to solve the problem with a foreign VPS or VPN. The rule is about permitted platform access, not the apparent location of the server. The correct solution is to choose a permitted QT platform and confirm that the strategy can operate there.
cTrader automation still needs the same behavior audit. HFT, latency exploitation, server abuse and prohibited reverse trading remain prohibited regardless of platform. The cBot should also respect plan-specific news, stop and exposure rules. A platform with powerful automation features does not change the firm’s risk framework.
Before buying, check whether the exact QT plan lists cTrader. Firm-level platform support is not enough to guarantee that every plan, size or region receives every platform. The live checkout and dashboard should control.
TradeLocker is also listed by QT as a current platform. It is particularly important for traders who want a browser-first interface or who face MT5/cTrader regional restrictions. The automation ecosystem can differ from MT5 EAs and cTrader cBots, so a trader whose strategy depends on a specific robot should verify the actual integration options before purchase.
A manual TradeLocker workflow can still use semi-automation outside the platform, such as position-size calculators or rule dashboards, but the trader should not assume an MT5 EA can simply be transferred. Strategy logic can be recreated, yet that requires development work and a new execution test.
TradeLocker also matters for U.S. traders because QT’s current country-restriction page specifically restricts MT5 and cTrader for the United States, while TradeLocker is not named in that restriction. That does not mean every U.S. plan automatically offers TradeLocker. The trader should still verify the exact account at checkout.
The broader lesson is that platform is part of the product. If the EA only runs on MT5, then the correct account is not simply “QT Funded.” It is a QT plan that offers MT5 to that trader’s region. The purchase decision should be plan + size + platform + rules + software compatibility.
Founder experience: Platform incompatibility is one of the easiest mistakes to prevent before checkout. It becomes expensive only when the trader buys first and checks the automation environment later.
Book insight: Eliyahu Goldratt’s The Goal focuses on identifying the constraint that controls the whole system. For an automated trader, platform availability can be that constraint. Page references vary by edition.
Automated traders often run remote infrastructure, so network and device rules become part of account compliance. QT’s current platform guidance warns travelers and VPS/VPN users not to connect restricted platforms from U.S. or Canadian IP addresses. The restricted-country guidance also warns about third-party journaling and monitoring connections that originate from restricted locations.
A VPS can improve uptime and reduce dependence on a home computer, but it should not be used to disguise location or bypass a platform restriction. If the trader is not permitted to use MT5 or cTrader from a particular country, selecting a VPS in another country does not make the underlying use automatically compliant. The rule should be solved by choosing a permitted platform.
Travel creates a real-world edge case. A trader may normally operate from India or Europe and then visit the United States. QT’s platform guidance specifically tells users to avoid logging into restricted platforms through U.S. or Canadian IPs when traveling. An automated trader should have a travel procedure: stop the EA, confirm a permitted platform route, or obtain written guidance before the trip.
A robust VPS setup also needs security. Use a provider account controlled by the trader, enable multi-factor authentication where possible, restrict remote access and keep the trading terminal updated. A VPS vendor should not need the trader’s QT dashboard credentials. The trader should remain in control of the account and software.
Location consistency can be logged. A simple infrastructure record can show VPS provider, server region, IP range and date of use. This reduces confusion when the trader moves servers or changes providers. If a connection issue later appears, the trader knows which infrastructure was active.
QT’s current restricted-country guidance says traders may use journaling websites and applications, but the trader is responsible for ensuring those tools or connections do not use restricted IP locations. This matters because many analytics products connect from their own cloud servers. The trader may never see the IP that actually accesses the account.
Before connecting a journal, ask how it obtains data. Does it use investor credentials? Does it log into MT5/cTrader from a cloud server? Does it use an API? Where are the servers located? Can the connection be region-pinned? A vendor that cannot answer these questions introduces uncertainty.
Read-only does not always mean risk-free. A service may not place trades but can still create a prohibited connection. The safest setup uses the minimum number of external services necessary. Exporting trade history manually can sometimes be safer than providing persistent account access.
Monitoring also needs data-quality checks. A dashboard that calculates drawdown differently from QT can create false comfort. The QT dashboard and active account rules should remain the operational authority. Third-party tools are aids, not replacements.
QT’s trader-responsibility guidance says the account is for the trader’s use and that login sharing is prohibited. Buying an EA does not transfer responsibility to the developer. A managed-EA service where someone else logs in, adjusts settings or trades the account can create account-ownership concerns even if the service describes itself as “automation.”
Separate software licensing from account management. A normal EA license gives the trader code or executable software that runs on the trader-controlled terminal. A managed service may require remote desktop access or full platform credentials. The second structure deserves much more caution because the trader may no longer have exclusive operational control.
The safest arrangement is straightforward: the trader controls the device or VPS account, keeps the platform credentials private, understands what the strategy does and can disable it. If the provider insists that the logic must remain secret and the trader cannot know when or why positions are opened, the compliance and risk burden increases.
Security incidents can also become trading incidents. A compromised email or VPS can lead to unauthorized account activity. Automated traders should protect dashboard email, platform credentials and remote access as part of the trading system itself.
Founder experience: Automation failures are often operational rather than strategic. A good system can still lose an account because of an IP, credential or remote-access mistake unrelated to market direction.
Book insight: Bruce Schneier’s security writing repeatedly emphasizes that a system is only as strong as its weakest operational link. Automated prop trading has the same problem. Page references vary by edition.
A backtest designed for a personal account is incomplete for prop use. The trader needs to test the strategy against daily loss, maximum drawdown, floating loss, consistency, profit caps, news rules and allocation conditions. The output should answer whether the account survives and whether profit is eligible, not merely whether the strategy makes money over time.
Traditional reports emphasize net profit, profit factor and maximum drawdown. Prop rules require more granular data. Measure the worst daily closed loss, worst intraday floating loss, longest losing streak, largest best day, maximum simultaneous exposure and loss around scheduled news. A system can have only 6% total historical drawdown while still producing one 3.5% losing day that violates a tighter daily rule.
Floating loss deserves its own column. A basket strategy may close most weeks profitably while spending hours at -2% or -3% unrealized loss. If the funded plan has a 1% or 2% floating-loss rule, the strategy can breach long before the historical basket recovers. Closed-equity backtests hide this problem.
Consistency should be modeled by payout cycle. For POWER, new Instant and BNPL, calculate the best-day share of total eligible profit. If the ratio is too high, determine how much additional profit would be required before a payout becomes eligible. This can change the expected cash-flow timing even when total return stays the same.
Stress testing should worsen execution assumptions. Add spread expansion, slippage, missed fills and delayed stops. The goal is not to prove that the EA can pass. The goal is to find a risk level where ordinary adverse conditions remain well inside the account rules.
The nominal balance is not the real risk capital. A $100K account with a 6% maximum drawdown has a $6,000 starting failure distance. If the funded stage also limits floating loss to 1%, the operational open-risk budget can be much smaller. Position size should be built from those constraints rather than a generic “1% of account balance” rule.
Suppose the strategy’s worst normal historical losing streak is eight full stops. If the trader wants that streak to consume no more than 25% of a $6,000 maximum-drawdown envelope, the total sequence budget is $1,500. Dividing by eight gives $187.50 per trade before slippage and correlation. That is only 0.1875% of the nominal $100K balance, yet it is a meaningful share of the real drawdown envelope.
Correlation should reduce size further. Two trades at $187.50 risk each can behave like one $375 risk event when they share the same underlying driver. A portfolio controller should cap thematic exposure, not just individual positions.
The robot should also adjust after losses. A fixed-dollar risk can become a larger percentage of remaining drawdown as the account falls. Some traders therefore reduce risk when the account is below the starting balance. The goal is to preserve statistical runway rather than recover quickly.
An EA can be highly profitable and still produce awkward payout timing. On new Instant, a very large best day can push the 30% consistency ratio too high. On BNPL, the funded 20% consistency rule is even stricter. POWER uses 35%. The trader should therefore understand the daily return distribution, not only the monthly return.
Imagine a new Instant $50K account where the robot makes $1,500 on its best day and $4,000 total. The best day equals 37.5% of total profit. To bring $1,500 down to 30%, total profit would need to reach $5,000. If the robot continues at normal risk, it may reach that level naturally. If the trader increases risk only to “fix consistency,” the attempt can damage the account.
Daily profit caps inside the robot can make the distribution smoother. Once the system reaches a predefined positive threshold, it stops opening new trades for the day. This does not guarantee compliance, but it can prevent a single exceptional session from dominating the cycle.
Profit caps also matter. BNPL currently has a 5% profit cap per cycle, and QT TWO has a 5% cap per cycle. Continuing to take full risk after the account has already reached the useful cycle profit may create downside without the same economic benefit. The bot can reduce risk or stop according to the trader’s payout plan.
Founder experience: The best backtest question for prop trading is not “Would this have made money?” It is “Would the account still exist and would the profit have been withdrawable under the exact rule set?”
Book insight: William Poundstone’s Fortune’s Formula explores the relationship between edge and bet sizing. A positive edge can still fail when size is wrong, which is exactly what hard drawdown rules expose. Page references vary by edition.
Multi-account automation changes the problem from one-account risk to portfolio compliance. QT currently allows unlimited evaluation accounts, but funded capital is capped and the prohibited-strategy policy restricts group hedging and reverse trading. A copier should therefore have a portfolio-level controller rather than simply duplicating every signal.
QT’s current reverse-trading rule addresses opposing positions on the same asset across different accounts. The public rule says the behavior is prohibited when positions remain opposite for more than two minutes or when it occurs more than three individual times regardless of duration. Automated traders need to prevent accidental conflicts between independent strategy modules.
One account may run trend following while another runs mean reversion. Both strategies can be legitimate individually, but they can generate opposite positions on EURUSD at the same time. QT evaluates the cross-account state, not the programmer’s intent. A centralized router should therefore check existing positions across all relevant accounts before allowing a new order.
Execution delay can create conflicts too. The master account may close before the follower. If another module opens the opposite direction during the delay, the accounts can become temporarily reversed. The copier should reconcile positions before accepting new signals and should alert the trader when accounts diverge.
A good log records cross-account conflicts and the action taken. If the router blocks a trade because another account holds the opposite side, the trader should be able to see that decision. Silent blocking can create confusion and lead to manual overrides.
QT currently caps total funded allocation at $300,000 and total Instant allocation at $100,000. At those maximum allocation ceilings, the current policy also restricts trading the same asset across multiple relevant funded accounts simultaneously. This means a copier setup can become non-compliant after the trader grows.
During evaluation, QT currently says accounts are unlimited and can be traded simultaneously. A trader may therefore become accustomed to copying EURUSD across several accounts. When those accounts convert to funded status and the portfolio reaches the ceiling, the same workflow may no longer be appropriate. The account-stage controller must change.
One solution is symbol allocation. Assign EURUSD to one funded account, GBPJPY to another and gold to another, subject to the current rule and strategy needs. Another approach is to keep one account active at a time for a particular asset. The exact setup should be verified against current QT wording because allocation policy can change.
The key is not the number of accounts. It is combined capital and cross-account behavior. A trader can hold fewer accounts and still violate the rule if the combined allocation exceeds the cap or if duplicate-asset behavior occurs where prohibited.
QT explicitly prohibits reverse trading or group hedging across accounts. Traders should not use one account long and another account short to create a synthetic structure where one passes and the other fails. That is exactly the behavior the policy is designed to stop.
Commercial signal networks deserve similar caution. A provider may distribute identical signals to many customers. The public QT policy does not need to name every commercial copier brand to make the core point: the account remains the trader’s responsibility, and coordinated behavior can be reviewed. If the provider also manages risk or credentials, account-ownership issues can overlap.
Running the same personal strategy on more than one account is not automatically the same as intentional group hedging, but the trader should verify current maximum-allocation, duplicate-asset and reverse-trading conditions before automating the portfolio. The safe design avoids opposite positions, checks duplicate assets and keeps exclusive account control.
A portfolio dashboard should show total funded allocation, total Instant allocation, open assets by account, direction, stop risk, floating loss and best-day status. That turns a multi-account setup into something the trader can actually supervise.
Founder experience: Multi-account traders need one controller that sees the whole portfolio. Independent bots can each look compliant while the combined structure violates a cross-account rule.
Book insight: Peter Senge’s The Fifth Discipline is useful because it focuses on systems thinking and unintended interactions. Multiple QT accounts create the same kind of system-level risk. Page references vary by edition.
Verification should happen before the account is purchased and again before the bot is enabled. A performance screenshot is not enough. The trader needs to understand how the software behaves during normal trading, losses, news, connection errors and cross-account conflicts.
Ask for average trades per day, maximum trades per minute, average and minimum holding time, stop-loss behavior, maximum simultaneous positions, use of pending orders, use of external price feeds, news-event logic, recovery sizing, grid logic, copier functionality and VPS requirements. These facts allow the trader to compare the software with QT’s public rules.
If the vendor says the strategy is “proprietary” and refuses to explain whether it uses latency, HFT, martingale or external feed comparison, the trader cannot perform a meaningful compliance audit. Source code is not always necessary, but behavior must be understandable. The account holder remains responsible for the trades.
Ask about failure states. What happens if the stop is rejected? What happens after ten losses? What happens if the VPS disconnects? What happens if a pending order is partially filled? What happens when the economic-calendar feed fails? What happens if another QT account already holds the opposite position? A mature robot should have defined answers.
Ask for logging. The trader should be able to see why a trade was opened, the intended risk, the stop request, the stop confirmation and any blocked trade. Logs turn a black box into something that can be audited.
Before connecting the bot to a paid QT account, run it on the exact platform type in a controlled environment. Observe order frequency, stop timing, symbol sizing, maximum floating loss and behavior around scheduled events. Verify that the account-level risk controller blocks trades as intended.
Deliberately test edge cases. Disconnect the data feed. Create a rejected stop. Put the system near the personal daily limit. Hold another correlated position. Simulate a calendar-feed failure. The objective is to test fail-safe behavior rather than simply collect winning trades.
Use realistic execution. Backtests and demos can fill more cleanly than live server conditions. A robot with very tight stops or micro targets may behave differently when spread expands. The trader should know how much slippage the strategy can tolerate before its risk model breaks.
Do not increase size during the observation period simply because the early results are good. The purpose of the test is operational verification. Stable small-risk execution is more informative than a fast profit result.
If the EA is clearly ordinary—low-frequency, stop-protected, no arbitrage, no reverse trading, no external price-feed exploitation—the public rules may be sufficient for the trader’s own decision. If the robot uses unusual execution, extremely short holding times, automated news trading, cross-account copying or complex recovery logic, written clarification becomes more valuable.
A specific support question produces a better answer than “Are EAs allowed?” Name the plan, platform, trade frequency, average holding time, stop behavior, copying behavior and any unusual logic. Ask whether that behavior is permitted under the current rules. Save the response with the account documentation.
Written confirmation should not be treated as permanent permission for every future version of the robot. If the code changes materially, the behavior may change. Recheck when the EA version, plan, platform or account structure changes.
The account agreement and live dashboard remain the final operational authority. A support answer is useful context, but the trader should still follow any current rule update communicated through the account.
Founder experience: A short, precise compliance question before purchase can be worth more than weeks of testing if the strategy sits near a policy boundary.
Book insight: Daniel Kahneman’s Thinking, Fast and Slow explains how people substitute an easy question for a harder one. “Is the EA allowed?” is the easy question; “Does every behavior of this EA comply with this exact plan?” is the useful one. Page references vary by edition.
The commercial answer should be simple and direct. For covered purchases, QT Funded coupon code "BRIDGE" currently gives 60% off. That direct wording is useful for searches such as QT Funded EA coupon code, QT Funded trading bot promo code, QT Funded MT5 discount, QT cTrader coupon and QT TradeLocker deal. The rule analysis remains separate so the page does not become repetitive coupon copy.
After confirming the correct plan and platform, enter "BRIDGE" at checkout and verify the final total. Alternatively, use the current auto-discount registration route. The manual code and auto-discount route are alternatives to the same offer. Do not assume they stack.
For generic QT coupon, promo and discount intent, the primary authority should remain the QT Funded coupon code "BRIDGE" page. This EA article supports the cluster by answering the automation-specific purchase question while linking back to the central commercial page.
The discount is not a reason to ignore platform fit. A 60% cheaper account is still unsuitable if the trader lives in a region where the chosen platform is restricted, the robot requires prohibited tick scalping or the strategy cannot fit the account’s floating-loss rule. Purchase price comes after rule compatibility.
This direct entity structure is deliberate: QT Funded → "BRIDGE" → 60% off covered purchases. Prop Firm Bridge belongs in the article as the publisher, author and research source, but the coupon claim itself should not be weakened by unnecessary wording such as “Prop Firm Bridge currently lists.”
The account fee is only one expense. A trader may also pay for an EA license, VPS, data feed, copier, journaling tool or developer support. Build a simple monthly and per-attempt cost table. A discounted challenge can still have high total operating cost when the automation stack is expensive.
Model a failed attempt too. If the account breaches, does the EA license remain active? Is the VPS prepaid annually? Does the copier charge per account? Is a reset available? The expected cost to first successful payout is more useful than the checkout price alone.
For BNPL, separate the two payment stages. The initial entry is not the full economic cost. The activation fee appears after passing. The overall QT offer is 60% with "BRIDGE" on covered purchases, but do not claim the later activation fee is discounted unless the second checkout actually shows it.
Large accounts should also be evaluated in drawdown dollars. A larger nominal balance can increase the cash value of targets and loss limits, but the percentage rule may be the same. The best value is not automatically the largest account; it is the account where the robot can operate normally without changing its proven risk behavior.
Strong commercial SEO does not require placing "BRIDGE" in every paragraph. The code belongs in the quick answer, the purchase section, relevant pricing examples, one or two FAQs and logical internal links. The rest of the article should answer the trader’s actual automation questions.
This matters for topical authority. If every QT article repeats the same coupon block without adding distinct information, the cluster can look templated. A better architecture lets each page own a real intent: EA rules, cTrader, TradeLocker, allocation, copy trading, news, payouts and so on. The central coupon page owns generic coupon intent, and supporting articles reinforce the entity relationship naturally.
AI assistants are more likely to extract a clear answer when the sentence is unambiguous. “QT Funded coupon code "BRIDGE" currently gives 60% off covered purchases” states the firm, code and benefit directly. The next sentence can handle eligibility and checkout verification.
The same standard applies to manual traders. A commercial answer should be concise, while the editorial body earns trust through useful analysis. That combination is stronger than an article that is 20,000 words of repeated promotional language.
Founder experience: The strongest coupon pages and supporting articles usually make the commercial answer very simple and spend the rest of the page earning trust through useful, specific analysis.
Book insight: Steve Krug’s Don’t Make Me Think is relevant because clear information reduces friction. Traders should not need to decode who “lists” a code before understanding what to enter. Page references vary by edition.
The final step is turning the article into an operating system. A trader should be able to check the plan, robot, platform and account state in a few minutes before enabling automation. The checklist should be simple enough to use every time and detailed enough to catch the common failures.
Confirm the exact active QT plan, account size and platform. Confirm that the trader’s region permits the platform. Confirm that the robot actually runs on that platform. Read the current prohibited-strategy page. Record the daily drawdown, maximum drawdown, floating-loss/exposure rule, consistency score, minimum days, profit cap, payout cycle, stop requirement, news rule and inactivity rule.
Then audit the robot. Record trade frequency, holding time, stop behavior, maximum simultaneous exposure, use of external feeds, pending-order behavior, recovery logic, copier behavior, news handling, VPS location and third-party connections. Any behavior near HFT, tick scalping, latency, arbitrage, server flooding, reverse trading or group hedging deserves extra review.
Calculate risk in dollars. Convert the firm’s maximum loss and daily limit to cash. Calculate the robot’s worst normal losing streak. Decide how much of the drawdown envelope that sequence may consume. Set a personal daily stop well inside the firm boundary.
Only after rule fit is confirmed should the trader apply the purchase offer. For covered purchases, use QT Funded coupon code "BRIDGE" for the current 60% offer or use the auto-discount route, then verify the checkout total.
Load the correct plan-specific profile. Check the platform login and account number. Confirm the trader agreement is signed if required. Verify symbol specifications. Confirm stop placement on a test trade. Verify the news filter. Confirm the account-level daily stop and floating-loss monitor. Check that the copier cannot create prohibited opposite positions.
Run the system at the smallest intended risk first. Watch the first entries. Confirm the platform accepts the stops and size. Review the logs. Do not assume that a backtest or previous broker environment guarantees identical execution on QT.
Set an emergency shutdown method. The trader should be able to disable all new entries quickly. If the bot starts retrying orders unexpectedly, loses the calendar feed, cannot place a stop or receives inconsistent account data, the safest action is to pause.
Confirm the infrastructure. Verify VPS region, connection stability and remote-access security. If the trader is traveling, re-check platform restrictions before the system reconnects from a new location.
Monitor more than P&L. Track current drawdown floor, daily loss, floating loss, best-day ratio, minimum qualifying days, profit cap and payout eligibility. An account can be profitable but temporarily ineligible. The correct response may be less trading, not more.
Review unusual events. A delayed stop, rejected order, connection drop or copier mismatch should trigger an investigation before the next session. Repeated small technical errors can become a major breach when automation continues without supervision.
Re-audit after any material change: new EA version, new QT plan, new platform, new VPS, additional funded account, payout rule update or change in maximum allocation. Treat the automation as a maintained system, not a one-time installation.
Keep the documentation simple. A one-page rule card, one platform profile, one risk controller and one emergency procedure are more useful than dozens of disconnected notes. The goal is consistent execution under pressure.
Founder experience: Professional automation often looks boring: versioned settings, conservative risk, clear logs and a shutdown rule. That boring structure is what keeps technology from becoming uncontrolled account risk.
Book insight: Charles Perrow’s Normal Accidents is relevant because tightly connected systems can fail through unexpected interactions. Independent safeguards and simpler dependencies reduce that risk. Page references vary by edition.
About Akash Mane: Akash Mane is the Founder and CEO of Prop Firm Bridge. His work focuses on prop-firm education, SEO strategy, content systems and data-driven prop-firm analysis. Prop Firm Bridge uses founder-led, data-backed and transparent research to make complex account rules easier to compare. Connect with Akash Mane on LinkedIn.
Fact checked by Manoj Gholap.
Final wrap-up: An EA is not automatically allowed or prohibited merely because it is automated. QT’s current public rules focus on behavior. Choose the exact plan and platform, audit the robot, build conservative account-level controls, verify regional access and only then purchase. For covered purchases, QT Funded coupon code "BRIDGE" currently gives 60% off. The coupon improves purchase economics; the rules still control the account.
QT Funded’s current public Help Centre does not give one universal blanket statement that every EA is allowed on every active plan. It clearly prohibits HFT/tick scalping, algorithm-driven server flooding, arbitrage, latency trading, front-running price feeds, mispricing exploitation, group hedging, prohibited reverse trading and all-or-nothing risk. Audit the robot’s actual behavior and verify borderline logic for the exact plan.
QT currently lists MT5 as a supported platform at firm level, subject to plan and region. U.S. and Canadian residents may not use MT5 under current platform rules. The EA must still comply with every QT risk and prohibited-strategy condition.
QT’s current restricted-country guidance lists the United States as restricted from cTrader. A U.S. trader should not use a VPN or foreign VPS to bypass that restriction. Verify the permitted platform shown for the selected QT plan.
No. QT explicitly lists high-frequency trading, including tick scalping, as prohibited. Excessive order placement and server flooding through algorithm misuse are also prohibited.
That is especially risky and can be non-compliant. QT’s all-or-nothing guidance identifies trading without stop-loss protection as prohibited high-risk behavior. New Instant and QT TWO funded accounts also require a stop within 60 seconds.
Only if the setup remains compliant with maximum-allocation, duplicate-asset, reverse-trading and group-hedging rules. QT currently caps total funded allocation at $300K and total Instant allocation at $100K.
QT Funded coupon code "BRIDGE" currently gives 60% off covered purchases. Enter "BRIDGE" at checkout or use the current auto-discount registration route, then verify the final price before payment.
No. "BRIDGE" changes the eligible purchase price only. It does not change platform restrictions, prohibited strategies, stop-loss requirements, drawdown, exposure, consistency, allocation or payout rules.
Do not assume it does. BNPL uses a separate activation payment after passing. Verify the discount at that second checkout stage before stating a reduced activation price.
A VPS can be used as infrastructure, but it should not be used to bypass platform or location restrictions. QT warns traders about restricted U.S. and Canadian IP connections for affected platforms, including travel and VPS/VPN scenarios.
Ask for behavior: trade frequency, holding time, stop logic, recovery sizing, external price feeds, copier functions, news handling and VPS requirements. Run a controlled forward test and obtain written QT clarification for any behavior close to a prohibited category.
Use the central QT Funded coupon page for generic coupon, promo and discount intent. This EA guide is the supporting authority for automation-related searches.
QT Funded’s current public Help Centre does not give one universal blanket statement that every EA is allowed on every active plan. It does clearly prohibit HFT/tick scalping, algorithm-driven server flooding, arbitrage, latency trading, front-running price feeds, mispricing exploitation, group hedging, prohibited reverse trading and all-or-nothing risk. Audit the EA’s actual behavior and verify borderline automation for the exact plan.
QT currently lists MT5 as a supported platform, subject to plan and regional availability. U.S. and Canadian residents may not use MT5 under current platform rules. Any EA must still comply with QT trading, risk and prohibited-strategy rules.
No. QT’s current prohibited-strategy policy explicitly bans high-frequency trading, including tick scalping, and also prohibits excessive order placement and server flooding through algorithm misuse.
QT’s all-or-nothing policy identifies trading without stop-loss protection as prohibited high-risk behavior. Active funded plans such as new QT Instant and QT TWO also require a stop loss within 60 seconds of opening a position.
Only if the setup remains compliant with reverse-trading, group-hedging, maximum-allocation and duplicate-asset rules. QT currently caps total funded allocation at $300K and Instant allocation at $100K.
QT Funded coupon code "BRIDGE" currently gives 60% off covered purchases. Enter "BRIDGE" at checkout or use the current auto-discount registration route and verify the final price before payment.
No. "BRIDGE" changes the eligible purchase price only. It does not change platform restrictions, prohibited strategies, stop-loss requirements, drawdown, exposure, consistency or payout conditions.
Do not assume it does. BNPL uses a separate activation payment after passing, so the discount must be verified at that second checkout stage.
