Spread is the one microstructure channel that survived the API audit - FX-available, and
genuinely historical in the tester ("During testing, the spread is not modeled but is taken
from historical data"), unlike swap (no history), signed tick flow (empty on FX) or depth
of market (absent on retail FX, never replayed).
test_spread.py measures four spread features against the triple-barrier label with the same
block-permutation null as test_volume.py. spr/atr - cost relative to the volatility a trade
must overcome - is the strongest reading anywhere in this project so far: significant on 5
of 8 instrument/geometry cells and 2-4x the magnitude of any volume feature.
Which immediately looked too good, because the label is computed WITH the spread charged
inside the barriers. A bar with high spread/ATR has its barriers shifted more adversely and
is mechanically likelier to resolve as a loss - so the feature would partly predict its own
cost model, which is not tradeable information.
Tested directly by relabelling at zero cost and re-measuring the identical feature:
EURUSD 2:3 +0.000655 -> +0.000485 (p 0.030 -> 0.066, loses significance)
EURUSD 1:2 +0.000626 -> +0.000399 (p 0.003 -> 0.017)
USDJPY 2:3 +0.000418 -> +0.000164 (never significant either way)
USDJPY 1:2 +0.000876 -> +0.000532 (p 0.003 -> 0.003)
XAUUSD 2:3 +0.000769 -> +0.000744 (p 0.027 -> 0.027)
XAUUSD 1:2 +0.000876 -> +0.000698 (p 0.003 -> 0.003)
So roughly 20-40% of it WAS the tautology, and the majority is not. What remains is a
volatility-regime reading: spread is near-fixed while ATR is not, so spr/atr is high
exactly when realised volatility is running below its own ATR estimate - which genuinely
predicts whether ATR-scaled barriers get reached at all.
Note what that does and does not buy. Like volume, spread is UNSIGNED: it informs Neutral
vs directional, never Buy vs Sell. It is the best-measured feature in this project and it
still cannot pick a side.
No EA changes in this commit - measurement only, and the MQL5 side already has an
uncompiled backlog.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The block fed exactly one number: (v[i] - v[i-1]) / v[i-1]. That is the first difference,
and it cannot express three things that matter - the LEVEL relative to a baseline (two
dead bars and two frantic bars both read ~0 change), and the two volume-vs-range
interactions, where heavy participation that went NOWHERE (absorption) and heavy
participation that travelled (continuation) mean opposite things and currently collapse
onto the same value.
research/test_volume.py measures each candidate's mutual information with the triple-
barrier label across 3 instruments x 2 geometries, against a BLOCK-permutation null -
blocks sized to the barrier horizon, because adjacent labels share almost their entire
outcome window and a free shuffle yields a null so tight that everything looks
significant. Finite-sample MI bias (~7/n here) is reported alongside rather than
subtracted, since the permutation null already absorbs it.
Result: volLevel beats the shipped change ratio outright on 4 of 6 cells (EURUSD 2:3
+0.000118 excess at p=0.006, USDJPY 1:2 +0.000284 at p=0.002); absorption is the single
strongest reading anywhere in the sweep at EURUSD 1:2 (+0.000404, p=0.002) though it is
null on XAUUSD; vol x range clears on 4 of 6. The shipped change ratio is itself
significant on 5 of 6, so it stays.
Kept OUT: a session-relative z-score against the same hour-of-day's own recent history.
It was the weakest candidate - null on both EURUSD cells - and it is the only one needing
per-hour rolling bookkeeping in MQL5. Not worth the state for a reading that did not
survive its own null on the primary instrument.
Magnitudes, stated plainly because they are the point: the excess MI is ~2e-4 nats against
a label entropy near 1.05. That is under a tenth of one percent of the label's
uncertainty. It is real, it repeats across instruments, and it is nowhere near an edge -
this is worth having because it costs one 50-bar loop, not because it changes the answer.
Prior work stands: the whole single-series feature family measured at the noise floor.
m_neuronsCount is already in the fingerprint, so the width change re-keys existing caches
by itself, which is correct - the input vector genuinely changed shape.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two defects surfaced by research/test_classic.py, both verified fixed by re-running the
transcription against 178k bars of EURUSD H1.
CSignalMA model 1 could never fire. For any recursive average - and MA_TYPE_EMA is the
shipped default - MA(i) = a*Close(i) + (1-a)*MA(i+1), so
DiffMA(i) = a * (Close(i) - MA(i+1))
DiffCloseMA(i) = (1-a) * (Close(i) - MA(i+1))
are positive multiples of one quantity and always share a sign. Model 1 asks for a close
BELOW a RISING average, which is precisely the combination that identity forbids: 0.000%
of bars, either direction, any symbol. The MQL5 standard library this was ported from
defaults to MODE_SMA, where the two are merely correlated - the bug arrived with the EMA
default, not with the port. Reading the slope one bar back (DiffMAPrev) breaks the tie for
every MA type while keeping the model's stated meaning. Now fires on 7.92% of bars.
CSignalIchimoku model 11 fired on 27% of bars at weight 100. Sanyaku is three standing
STATES conjoined with no transition term, so it held across long stretches - and being
last in the if-chain at the top weight, the module's highest-conviction reading was also
its most common one, overwriting all eight event models below it on a quarter of all bars.
The old comment rejected an event form because "demanding all three flip on the same bar
would fire almost never" - true, but that is not the alternative. Kouten is the TURN: the
ALIGNMENT transitions, and only one role need change for it to. Testing !Sanyaku(idx+1)
fires once per aligned stretch. Now 2.17%, in line with Kumo breakout (2.4%) and the
strong TK cross (1.1%). DataReady() extended one bar deeper to cover the lookback.
Neither pattern showed edge before or after; this is about the models meaning what they
say and the vote not being dominated by a constant.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Transcribes all 26 classic vote models (MA 4, RSI 4, MACD 6, Ichimoku 12) from
Signals/*.mqh into vectorised Python, with their shipped constructor weights, then tests
them as entry triggers on 178k-bar FX histories.
Pre-registered by construction: the rules were written long before this test and nothing
about them is fitted here, so there is no in-sample/out-of-sample split to draw and the
whole history is usable. Break-even == chance by the gambler's-ruin identity, so "beats a
coin" and "makes money" are one question. Sequential non-overlapping trades only; a
sign-flip null over the whole pattern family gives the family-wise bar.
The result that matters is a negative one, and it took two lookahead fixes to see:
- _price_extremum reproduced the standard library's CENTRED MinValue(pos-2,5) window,
which reads up to 2 bars newer than the extremum it describes.
- turning_points marked a turn AT bar i, which is only knowable once bar i+1 closes.
Together those two bars of leakage WERE the entire apparent edge. MACD_p4 on EURUSD 1:2
read +5.05pp at +4.05 sigma before, -0.02pp at -0.02 sigma after; USDJPY 1:2 went +5.24pp
-> +0.02pp. RSI_p2's large NEGATIVE went the same way (-10.33pp -> -1.34pp), which is the
tell: a leak inflates whatever sign it lands on.
With both closed, across 4 instruments x 3 geometries: no pattern, no vote threshold, no
quorum and no event+confirmation rule separates from chance. One cell in ~180 tests stars
(SP500 2:6 vote>=30) and it is non-monotone in the threshold either side of the hit.
test_exits.py answers the trade-management half with the control that makes it mean
something: hold entries fixed, vary only the exit, and run every rule again on RANDOM
entries at the same bars. Breakeven-at-1R, chandelier trails, partials and time stops all
move E[R] - and move it by the same amount on random entries. No rule beats its own
control (max +0.99 sigma over 32 comparisons). Management reshapes the win-rate/payoff
split; it does not manufacture expectancy from a directionless entry.
Residual E[R] across every cell is -0.01 to -0.08 R, which is approximately the spread.
Incidental, both worth fixing in the EA:
- CSignalMA pattern 1 is unsatisfiable at the shipped EMA default. For an EMA,
MA[i]-MA[i-1] and c[i]-MA[i] are both positive multiples of (c[i]-MA[i-1]), so
"close below the MA while the MA rises" cannot occur. Dead code (weight 10).
- Ichimoku pattern 11 (Sanyaku, weight 100, the method's top signal) fires on 27% of
bars because it is a conjunction of three standing STATES with no event term, so it
dominates the averaged vote while carrying no trigger information.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Keeps the Python that turned a per-hypothesis cost of minutes into
seconds, so the next person (or the next me) can re-run any of this
without MetaTrader in the loop.
kit.py vectorised triple-barrier labeller + scale-free feature set.
The labeller is a faithful port - stop tested before target
within a bar, so a bar spanning both scores as the loss -
and reproduces the EA's own distribution to 0.05pp
(24.93/22.01/53.06 vs 24.9/22.0/53.1) at 12x the speed.
wf.py purged, embargoed walk-forward gradient boosting.
sim.py sequential NON-OVERLAPPING trade simulation.
sweep.py the cell x geometry sweep.
detail.py full threshold profile for one configuration.
WHAT IT FOUND, and the order matters because the first answer was wrong:
Naive walk-forward looked like an edge - precision rising monotonically
with model confidence, 24.4/24.6/25.3/26.2/27.1%, topping out at +2.66pp
and 2.9 sigma. All of it pseudo-replication: a 128-bar barrier means
adjacent bars share almost their whole outcome window, so one trade was
being counted up to 128 times. Counting each trade ONCE (sim.py) the
ordering collapses to 25.9/31.1/26.8/28.1/25.8 and nothing is
significant. Same error family as the MI null that assumed independence.
That exposed a structural problem bigger than the result: at a 128-bar
horizon, 18 years of SP500 H1 yields at most ~300 independent trades,
which can only resolve a +6pp edge at 2 sigma. Real edges are 1-3pp. The
shipped configuration is not merely unproven - it is statistically
UNFALSIFIABLE on the available history.
The cost/power screen then showed SP500 is among the worst cells
available: 54k bars and spread/ATR 0.34, against EURUSD/USDJPY at 178k
bars and 0.05. Weeks of training went into the hardest instrument on the
list, 3x less data and 7x the relative cost.
Final sweep - 4 instruments x 3 geometries, purged walk-forward,
independent trades: nothing clears +2 sigma. The one survivor (EURUSD
2:3 h48, +2.76pp at +1.57 sigma) dissolves under its full threshold
profile: non-monotone across thresholds, and its per-fold win rate decays
monotonically through time (52.9 -> 45.9 -> 41.7 -> 35.2 -> 26.2).
Conclusion: with price/volume-derived technical features there is no
tradeable entry-direction edge on these instruments - now tested with a
model class that finds interactions, on 3x the data, with honest
statistics.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
