I tested Quantum Bitcoin EA v3.2 on BTCUSD H1 from 1 January 2022 to 25 April 2026 in the MetaTrader 5 Strategy Tester on 100% real ticks. The headline result is strong: about $3,934 net profit on a $10,000 account, a Profit Factor of 2.69 and a 77.6% win rate across 975 trades, with both long and short sides in profit.
The more important risk number is equity drawdown. Balance drawdown was 2.37%, while equity drawdown reached 13.83%. Reconstructing the grid cycles from the deal history shows why the gap is so large: some baskets added several same-direction positions and remained open for weeks while carrying floating losses.
The MQL5 description says the grid is designed so that every trading cycle concludes with a win. In this test, all 587 reconstructed cycles had positive realised P/L before commission and swap, but 5 finished negative after account costs. That difference between trade P/L and final account contribution is central to the review.
Key findings
- It is a grid. The order comments run QB (Step: 1), (Step: 2), (Step: 3) and so on. I reconstructed 587 complete trading cycles, with additional same-direction positions appearing as price moved against the first entry.
- Balance drawdown alone understates floating equity risk. Maximum balance drawdown was 2.37% ($310.76); maximum equity drawdown was 13.83% ($1,813.13) — roughly 5.8 times larger. The open-equity path was much rougher than the balance curve suggests.
- Most cycles closed on the first entry. About 62% of cycles closed without requiring a Step 2 entry. The remaining 38% required at least one additional position.
- “Every cycle wins” depends on how a win is counted. All 587 cycles were positive before commission and swap; after account costs, 582 remained positive and 5 were negative. The MQL5 description does not define whether a win is measured before or after costs.
- Costs are material. Commission (−$729.92) and swap (−$293.38) removed more than $1,000 from the pre-cost trade result over the test.
- Broker environment matters. This test ran on RannForex-Server; the MQL5 page recommends IC Markets / IC Trading. The commission, swap and BTCUSD conditions here describe the tested environment and may not reproduce on another broker.
- The deepest observed basket reached Step 7. InpGridMaxTrades was configured at 10, but the historical test itself only establishes the observed depth.
What tested
This is an independent EA ForexLab test of Quantum Bitcoin EA for MetaTrader 5, listed on the MQL5 Market by Bogdan Ion Puscasu. The current product page describes it as a BTCUSD H1 trend-following strategy with grid-based position management and says the grid is designed so that every trading cycle concludes with a win. It currently recommends a $1,000 minimum deposit, 1:500 leverage, a hedge account and a low-spread ECN/Raw/Razor account.
I focused on what the headline report does not show: grid depth, cycle structure, trading costs, holding time and floating equity exposure. The methodology behind the test is described in the EA ForexLab testing methodology for trading algorithms.
Test setup
Every value below comes directly from the Strategy Tester report. The test ran on RannForex-Server (build 5833) with the configuration string Quantum Bitcoin EA v3.2 (16/02/2026). The Strategy Tester reports 100% real-tick history across 31,900 bars and approximately 140.9 million ticks.
One input worth flagging: InpSlippage=10 is an EA/test input. It is not evidence that realistic historical market slippage was modelled, and I do not treat it as such.
I ran this test on RannForex-Server, while the MQL5 page currently recommends IC Markets or IC Trading. The commission, swap and BTCUSD contract conditions analysed here therefore describe the tested RannForex environment and should not be assumed to reproduce unchanged on another broker.
| Parameter | Value |
|---|---|
| Expert | Quantum Bitcoin EA_3.2_fix |
| Version input | Quantum Bitcoin EA v3.2 (16/02/2026) |
| Platform | MetaTrader 5 |
| Server | RannForex-Server (Build 5833) |
| Symbol | BTCUSD |
| Timeframe | H1 |
| Period | 2022.01.01 – 2026.04.25 |
| History quality | 100% real ticks |
| Modelled bars / ticks | 31,900 bars / 140,869,440 ticks |
| Initial deposit | $10,000 |
| Leverage | 1:500 |
| Risk levels (InpRiskLevels) | 3 |
| Initial lots (InpInitialLots) | 0.01 |
| Lots multiplier (InpLotsMultiplier) | 1.2 |
| Lots max (InpLotsMax) | 1.2 |
| Take profit (InpTakeProfit) | 10000.0 |
| Grid distance % (InpGridDistPct) | 90.0 |
| Grid distance multiplier (InpGridDistMultip) | 1.4 |
| Grid max trades (InpGridMaxTrades) | 10 |
| Min orders real BE (InpMinOrdersRealBE) | 3 |
| Trading window (Start–End) | 08:00 – 22:30 (server) |
| Trade Friday / NFP / Holidays | false / false / false |
| Minutes between trades | 60 |
| Slippage input (InpSlippage) | 10 (EA/test input — not modelled market slippage) |
MT5 backtest results
The headline Strategy Tester statistics are strong.
Net profit was $3,934.46 on a $10,000 start, with a Profit Factor of 2.69 and 757 of 975 trades closing in profit. Both directions were profitable: shorts won 78.04% of 542 trades and longs won 77.14% of 433. The largest single winner was $223.87 and the largest single loser −$78.91, so no individual trade dominated the result.
| Metric | Result |
|---|---|
| Total net profit | $3,934.46 |
| Final balance | $13,934.46 |
| Gross profit | $6,263.46 |
| Gross loss | −$2,329.00 |
| Profit factor | 2.69 |
| Expected payoff | $4.04 |
| Recovery factor | 2.17 |
| Sharpe ratio | 1.54 |
| Total trades | 975 |
| Winning trades | 757 (77.64%) |
| Losing trades | 218 (22.36%) |
| Short trades (won) | 542 (78.04%) |
| Long trades (won) | 433 (77.14%) |
| Largest profit trade | $223.87 |
| Largest loss trade | −$78.91 |
| Average profit trade | $8.27 |
| Average loss trade | −$9.01 |
| Balance drawdown maximal | $310.76 (2.37%) |
| Equity drawdown maximal | $1,813.13 (13.83%) |
| Equity-to-balance drawdown ratio | ≈ 5.8× |
I include both drawdown figures because they describe different parts of the risk. For a multi-position grid, the distinction matters.
Why I care more about equity drawdown than balance drawdown
Balance reflects realised account results, while equity also reflects open floating P/L. A grid can amplify the difference because several positions may remain open simultaneously while a basket moves against the strategy. Here the two curves diverged sharply.
Maximum balance drawdown was 2.37%. Maximum equity drawdown was 13.83%, a ratio of about 5.8 to 1. The closed balance therefore looks much smoother than the equity path: the open-position risk reached a level the balance curve does not capture.
The two drawdown figures measure different things. For this grid, equity drawdown is the more informative historical risk measure because it includes floating P/L from open baskets. The observed 13.83% should not be treated as a future maximum.
How Quantum Bitcoin EA actually builds a grid
I reconstructed the grid structure from the order comments and the transaction history. Each observed cycle starts with a Step 1 entry, and additional same-direction Step entries appeared as price moved against the existing basket. The inputs include grid-distance and distance-multiplier parameters, but the deal history does not expose the exact internal trigger. Positions within each reconstructed cycle closed together at a common exit price; the report shows the observed behaviour, not the exact internal exit rule.
The position-level win rate and the cycle-level result measure different things. A multi-entry basket can contain both winning and losing positions and still finish positive as a group. That is how a 77.64% position win rate can coexist with 587 reconstructed cycles that were all positive before commission and swap. In total, 218 individual positions closed at a loss.
How often the grid needed additional entries
Grid frequency and depth are two important risk diagnostics, so I measured both. The table below is the deepest Step each cycle reached before closing.
| Deepest Step Reached | Cycles | Share |
|---|---|---|
| Step 1 (single entry) | 365 | 62.18% |
| Step 2 | 128 | 21.81% |
| Step 3 | 51 | 8.69% |
| Step 4 | 21 | 3.58% |
| Step 5 | 16 | 2.73% |
| Step 6 | 5 | 0.85% |
| Step 7 | 1 | 0.17% |
| Total | 587 | 100% |
About 62% of cycles closed without a second entry. Most of the rest reached Step 2 or 3. Only 6 cycles reached Step 6 or deeper, and exactly one reached Step 7. The reconstructed cycle chronology showed no overlapping baskets. Maximum simultaneous exposure was seven positions, reached by the single Step 7 cycle.
Most cycles ended after Step 1, but 37.82% required at least one additional entry. Deeper baskets deserve particular attention because they carry more simultaneous exposure and can remain open long enough for financing costs and floating drawdown to become material.
Does every Quantum Bitcoin trading cycle really win?
The vendor’s cycle claim can be checked directly against the reconstructed ledger. I calculated each cycle two ways: realised trade P/L before commission and swap, then final account contribution after those costs.
All 587 reconstructed cycles had positive realised P/L before commission and swap. After account costs, 582 remained positive and 5 were negative. The MQL5 description does not define whether a “win” is measured before or after trading costs.
Put concretely: if a winning cycle is defined before commission and swap, all 587 qualified in this historical test. If it is defined by final account contribution, 5 of 587 did not.
| Metric | Result | Interpretation |
|---|---|---|
| Total reconstructed cycles | 587 | Reconstructed chronology showed no overlapping baskets |
| Positive before commission & swap | 587 (100%) | Every cycle had positive realised P/L before trading costs |
| Net-positive after costs | 582 (99.15%) | The grid still resolved profitably on the large majority |
| Net-negative after costs | 5 (0.85%) | Costs flipped five raw-positive cycles; swap decisive in four, one already negative after commission |
| Maximum observed Step | 7 | Reached once (a 63-day sell basket) |
| Configured InpGridMaxTrades value | 10 | Observed baskets reached no deeper than Step 7 |
Commission and swap change the answer
Here is the full reconciliation, straight from the deal ledger. Aggregate trade profit and loss before costs was +$4,957.76. Commission took −$729.92. Swap took −$293.38. Net result: +$3,934.46, which matches the report to the cent. The $4,957.76 figure is the net sum of the MT5 Profit column before commission and swap. It is not the Strategy Tester Gross Profit metric of $6,263.46, which sums profitable trades only.
Commission accumulated to −$729.92 across the 1,950 deals in this test. Swap added −$293.38. Swap can accumulate when positions remain open across the broker’s financing periods, which makes prolonged baskets especially sensitive to holding costs. Trading costs turned five raw-positive cycles into net-negative cycles: swap was the decisive cost in four of the five, while one cycle was already negative after commission.
This test also shows why trading costs matter for the strategy. On an eligible broker and account, a forex rebate can reduce part of eligible commission. It does not reduce swap or grid exposure, and I do not assume the RannForex commission in this test is rebate-eligible.
The deepest grid sequence in the test
The deepest sequence was a Step 7 sell basket. Its first position opened on 23 June 2025 and the group closed on 26 August 2025 — a basket span of about 63 days from first entry to final closure.
The entries were sized 0.01, 0.01, 0.01, 0.02, 0.02, 0.02 and 0.03 lots as the grid deepened, opened at prices climbing from roughly 102,500 up to 117,600 as BTC ran against the position. By the time all seven positions closed together near 109,600, BTC had retraced substantially from the highest grid-entry area. The exact internal exit condition is not exposed by the report.
The basket closed with +$53.51 in trade P/L before costs but finished at −$37.25 after costs. The prolonged holding period accumulated $77.57 in swap, and commission added another $13.19. Basket duration and position duration should also be kept separate: this basket spanned about 63 days from first entry to final closure, while the longest individual position anywhere in the test lasted 1083 hours 33 minutes, about 45 days.
Position sizing and the 1.2× multiplier
The inputs set InpInitialLots=0.01, InpLotsMultiplier=1.2 and InpLotsMax=1.2. Realised positions ranged from 0.01 to 0.05 lot; most were 0.01 or 0.02. InpLotsMax was configured at 1.2, while the largest realised position in this test was 0.05 lot. I do not infer the exact internal meaning of InpLotsMax from the parameter name alone.
I would classify the observed structure as a progressive averaging grid rather than a classic 2× martingale. In this backtest, the main exposure increase came from accumulating same-direction positions rather than a doubling lot sequence. That distinction does not remove grid risk: exposure still rises as additional positions are added.
There is also an anomaly in the sizing worth recording. The configured InpInitialLots value is 0.01, yet every Step 1 entry in 2022 opened at 0.02 lot. From April 2023 onward, Step 1 used 0.01. The cause is not established by the test, so I do not assign one. What is confirmed is a mismatch between the configured initial lot and the realised early-period order size.
Long vs short
Both directions were profitable. The report shows 542 short trades with a 78.04% win rate and 433 long trades with a 77.14% win rate. Reconstructing the account contribution by direction gives about +$2,339.78 from sell cycles and +$1,594.68 from buy cycles after costs.
| Direction | Positions | Win Rate | Derived Net Contribution | Notes |
|---|---|---|---|---|
| Short (sell) | 542 | 78.04% | +$2,339.78 | More cycles and positions over the period |
| Long (buy) | 433 | 77.14% | +$1,594.68 | Fewer opportunities, near-identical win rate |
| Both | 975 | 77.64% | +$3,934.46 | Net positive on each side after costs |
The larger sell contribution does not establish better short-side behaviour. Sell cycles were more numerous over the test, while the long and short win rates were almost identical. Both directions remained net positive after costs, so the total result was not produced by one side offsetting a losing opposite side.
Trade duration
This is not a scalper. The report puts the minimum position holding time at 56 seconds, the average at about 24 hours 36 minutes, and the maximum at 1083 hours 33 minutes — roughly 45 days.
The distribution is concentrated in the hours but has a long tail. A position held for days or weeks remains exposed to BTC price movement for longer, and financing costs can accumulate when positions remain open across the broker’s financing periods. Long duration alone does not determine whether a position finishes profitable or losing. It matters here because deeper baskets can combine prolonged market exposure, accumulated swap and larger floating losses.
Trading hours and weekend behaviour
The inputs set InpStartTime=08:00 and InpEndTime=22:30 in server time, with InpTradingFriday=false. Because BTCUSD trades through the weekend in this test environment, I reconstructed activity by hour and weekday to compare the observed orders with those settings.
All 587 Step 1 entries occurred between 08:00 and 22:00 server time, while Step 2 and deeper entries also appeared outside that window. This pattern is consistent with the configured time window applying to new-cycle initiation while existing baskets continue to be managed.
Friday is the clearest case. No Step 1 entry occurred on a Friday, while additions and exits did, and new cycles opened on Saturdays and Sundays. This is consistent with InpTradingFriday=false affecting new-cycle initiation rather than all basket activity.
Performance by year
I grouped realised deal P/L, commission and swap by the calendar year in which each account transaction was booked.
| Year | Net Contribution |
|---|---|
| 2022 | +$524.16 |
| 2023 | +$281.20 |
| 2024 | +$1,576.16 |
| 2025 | +$1,346.90 |
| 2026 (partial to 25 Apr) | +$206.04 |
Every displayed period contributed positively, but the profit was unevenly distributed. 2024 and 2025 contributed most; 2022, 2023 and the partial 2026 were much smaller. There was also a long inactive interval: no new Step 1 cycle opened between 22 September 2022 and 12 April 2023 — roughly 202 days. Five positive calendar segments are useful evidence, but they are not proof of robustness across market regimes.
Vendor claims vs test evidence
The table below compares the current MQL5 positioning with what the independent test establishes.
| Vendor Positioning | Independent Test Evidence | Assessment |
|---|---|---|
| BTCUSD H1 | Test ran on BTCUSD H1 | Matches |
| Trend-following strategy | Entry and exit logic cannot be reconstructed sufficiently from the trade history to verify the trend-following classification | Not independently verified |
| Grid position management | Step 1–7 order sequences across 587 cycles | Strongly confirmed |
| Each cycle closes favourably | 587/587 reconstructed cycles had positive realised P/L before commission and swap; 582/587 remained positive after costs. The MQL5 wording does not define the accounting basis of “win” | Definition-dependent |
| Minimum deposit $1000 | This test used $10,000 (10× the stated minimum) | Not validated by this test |
| Leverage 1:500 | Test ran at 1:500 | Matches |
| Hedge account | Observed execution used multiple same-direction positions; account mode is not explicitly stated in the Strategy Tester summary | Consistent with the vendor recommendation; not independently established from account metadata |
| Low-spread ECN/Raw recommended | Spread sensitivity was not isolated in this study; commission and swap drag were material | Not directly tested |
What I would test next
The historical test answers several structural questions, but live transferability still needs separate validation. I would prioritise the following tests.
- Forward test on the intended broker and account. This is the next direct check of current execution conditions: spread, charged commission and swap, slippage behaviour and grid-depth frequency. If the goal is to measure actual live execution rather than demo execution, the measurement needs to come from a live account. The EA ForexLab guide explains why forward testing an Expert Advisor matters.
- Capital sensitivity: $1,000 vs $10,000. The test used $10,000 while the MQL5 page states a $1,000 minimum. If the same absolute position-sizing behaviour were used on $1,000, percentage exposure and margin pressure would be materially different. The $1,000 case therefore needs a dedicated backtest rather than an extrapolation from this one.
- Adverse-move stress. The deepest basket in this test reached Step 7. A sustained adverse BTC move is the most relevant scenario for testing whether the strategy can progress beyond the historical maximum observed here.
- Broker comparison. BTC contract specifications, financing costs and spreads can differ materially between brokers. That matters for a grid that can keep positions open for weeks.
- Parameter sensitivity. Grid distance, the distance multiplier, the lot multiplier, the InpGridMaxTrades setting and the trading-hours window all affect the observed exposure pattern and are worth testing systematically.
Who this EA may suit — and who it may not
This is a description, not advice, and nothing here is a recommendation to buy or to trade.
The profile is more relevant to experienced grid users who monitor floating equity, understand prolonged BTC exposure and are prepared to validate broker-specific costs. It is a poor fit for anyone judging the system from win rate or balance drawdown alone, or for anyone uncomfortable with several same-direction positions being held at once. This $10,000 test should not be used as validation of the vendor’s $1,000 minimum.
Final verdict
The historical result is strong under the tested conditions: $3,934.46 net profit, Profit Factor 2.69, a 77.64% trade win rate, positive contribution from both directions and positive account contribution in every displayed calendar period. The deepest observed basket reached Step 7, while InpGridMaxTrades was configured at 10.
The risk profile is still a grid risk profile. Equity drawdown was almost six times the balance figure, and some baskets remained open for weeks. Trading costs turned five raw-positive cycles into net-negative cycles, with swap the decisive cost in four of them. A longer adverse BTC move remains an important untested stress scenario, particularly because the deepest historical basket already reached seven positions and lasted about 63 days. The $10,000 backtest also does not establish the risk profile of the vendor’s $1,000 minimum.
My conclusion is straightforward: Quantum Bitcoin EA should be evaluated by its equity exposure, grid depth and cost structure rather than balance drawdown alone. The backtest documents a positive historical result, but it does not show how the same grid will behave under current live broker conditions. The next useful step is a forward test on the intended broker and account.
The independent test conditions and configuration are catalogued in the Forex EA Database. For a structurally comparable grid from the same product family, the Quantum Athena EA review covers the gold-side sibling and the same balance-versus-equity question.
Test archive
This review is based on a MetaTrader 5 real-tick Strategy Tester run of Quantum Bitcoin EA v3.2 on BTCUSD H1 from 2022 to 2026. I analysed the complete deal history and reconstructed the grid cycles, trading costs, position depth and timing from those transactions. Vendor positioning is referenced to the current official MQL5 product page.
This article documents an independent backtest for research purposes. It is not financial advice, not a recommendation to buy, and not a prediction of future results. Automated trading on a leveraged, volatile instrument like Bitcoin can lose money.