1. The expectancy stop was stone dead at shipped defaults. Its only feed -
RecordTradeResult inside CTradeJournalManager::Update() - ran solely under
UseDatabaseRanking, which ships false, so the da54639 halt was armed
(ExpectancyMinTrades=40) and never received a single closed trade. A risk
rule must not be a side effect of an analytics toggle: the journal gains
InitTrackingOnly(), Update() runs unconditionally from OnTick and skips
only the DB insert when no DB was initialized.
2. Below-minimum lots were silently bumped UP to SYMBOL_VOLUME_MIN by
TCNormalizeVolume - correct for a user-entered fixed lot, but in the
risk-sizing path it turned a budget-capped 0.05 into 0.10 on min-0.10/
step-0.01 symbols: double the intended risk, after CapRiskAmount already
clamped, exactly the routine-stop-out-breaches-the-daily-limit scenario
the budget exists to close. CMoneyRiskBase now refuses the trade when the
risk-derived lot is below the broker minimum.
3. All trading was async fire-and-forget (SetAsyncMode(true)) with no
OnTradeTransaction handler and no retry: server retcodes were never
observed. Fail-safe for entries, not for closes - a silently rejected
close rode the position until the next bar (or next day for the timed
close window). Now synchronous, matching the risk-budget flatten's own
already-synchronous CTrade; on an H1 EA the latency is irrelevant.
4. FIXED_LOT bypassed the budget entirely (no CapRiskAmount, no
OpenRiskAtStops) - pre-halt it could commit more than the remaining daily
allowance. A fixed lot cannot be scaled, so the rule is binary: its
loss-to-stop fits the remaining allowance whole or the trade is refused;
unpriceable risk (no SL) is refused while the budget is enabled.
Compile: 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The daily (4%) and total (8%) rules bound how FAST an account can lose. Nothing
noticed WHETHER it was losing. A negative-expectancy signal traded at 1% inside
that envelope breaches no rule and still arrives at zero - it just takes longer,
with every limit green the whole way down. That is the realistic way this EA
destroys an account, and no existing guard could see it.
THE ARITHMETIC THIS ENFORCES. Expected value per trade is p*TP - (1-p)*SL - cost.
With no directional edge p equals SL/(SL+TP), which is also the break-even rate,
so the payoff terms cancel exactly and EV = -cost. Expected P&L is -(trades) x
cost: strictly negative, proportional to activity. Measured here: directional
precision 23-24% against a 25% break-even, flat across every confidence tier,
with 58 points of spread on SP500. Sizing, stop placement and trailing move
variance around that mean; none of them changes its sign.
So every closed position now reports its result in R (net profit over money
actually at risk) and the running mean is tested against zero. Above the
configured minimum sample, if mean + sigma*SE < 0, new entries stop.
- SIGNIFICANTLY below, not merely below. A run of losers is ordinary variance
even for a profitable system; halting on the raw mean would be the same
act-on-noise error the MI gates exist to prevent. Using the standard error
means a wide spread simply demands more trades before the rule can fire.
- NET of swap and commission (ResolveClose already sums all three). Deliberate
and load-bearing: when the edge is zero, cost IS the expectancy, so a gross
version would measure a strategy nobody can trade.
- Reported in R so symbols, lot sizes and balances share one scale and one
mean. Trades without a stop are not scored rather than assigned a guessed R.
- LATCHED across restarts, like the daily halt and for the same reason: a
latch a reattach clears is not a latch. Clearing it means deleting the risk
state file, deliberately, after looking at why.
State is appended to the risk file length-guarded, so files written before this
still load and start their sample at zero rather than misreading.
Defaults 40 trades / 2 sigma; ExpectancyMinTrades = 0 disables it.
This does not make the strategy profitable and is not meant to. It stops paying
tuition on one the results say is losing, and does it on measurement rather than
on a drawdown limit finally being reached.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- Added optional `weighScale` parameter (default -1.0) to `CNeuronBase::Init` and `CLayer::CreateElement`.
- Updated `CNeuronPool::Init` to use LeCun-uniform scaling (1/sqrt(window+1)) for its base initialization.
- Updated `CNet::CNet` to use He-scaled initialization (sqrt(2/neurons)) for dense layers.
- These changes enable more flexible and statistically sound weight initialization, matching the rationale used in OCL-based implementations, leading to better training stability and convergence.
