mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-13 08:08:05 +00:00
231 lines
29 KiB
Plaintext
231 lines
29 KiB
Plaintext
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ËI£iùG ã ó² • S r SSKJr SSKrSSKJs Jr SSK r
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SSK r SSK
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rSSKrSSKJrJrJrJrJrJrJrJrJrJrJrJ r J r J r J r Sr Sr!Sr"Sr#\ S S
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S S 4 S!S
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jjr$\$" \!5 r%\ RL " \%SS9r'\"\#SS. S"S jjr( " S S5 r) " S S5 r* " S S5 r+ " S S5 r, " S S 5 r- " S S 5 r. " S S 5 r/g)#uÚ test_pandas_ta_parity.py — Validate quantalib vs pandas-ta using the same
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methodology as our C# ValidationHelper:
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1. Generate a LONG GBM series (5000 bars, seeded) so recursive indicators converge.
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2. Compare only the LAST 100 values (DefaultVerificationCount = 100).
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3. Skip lookback/warmup bars before comparison window.
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4. Tolerance: 1e-7 default, looser for known algorithmic differences (SPEC §9.3).
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This mirrors lib/feeds/gbm/ValidationHelper.cs exactly.
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é )Ú annotationsN)ÚsmaÚemaÚdemaÚtemaÚwmaÚhmaÚtrimaÚalmaÚrsiÚrocÚmomÚstddevÚvarianceÚzscoreÚbbandsé* é' éd ç•Ö&è .>g Y@gš™™™™™©?gš™™™™™É?gAAp?c óÆ • [ R R U5 nUR U S-
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sss& [ R " U SS U SS S9 X+S' U $ )zsGenerate GBM close prices matching C# GBM feed logic.
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S(t+1) = S(t) * exp((mu - sigma^2/2)*dt + sigma*sqrt(dt)*Z)
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é g à?é )Údtyper N)Úout)
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ÚnpÚrandomÚ default_rngÚstandard_normalÚsqrtÚemptyÚfloat64ÚcumsumÚlogÚexp) ÚnÚseedÚ start_priceÚmuÚsigmaÚdtÚrngÚzÚdriftÚ diffusionÚ log_returnsÚpricess Ú9c:\github\quantalib\python\tests\test_pandas_ta_parity.pyÚ
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R " U5 5 eS ng )!zÈCompare only the last `verify_count` values where both are finite.
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This matches the C# pattern:
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int start = Math.Max(0, count - skip);
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for (int i = start; i < count; i++) { ... compare ... }
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©ú==)zN%(py3)s
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{%(py3)s = %(py0)s(%(py1)s)
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} == %(py8)s
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{%(py8)s = %(py5)s(%(py6)s)
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}ÚlenÚqtl_arrÚpta)Úpy0Úpy1Úpy3Úpy5Úpy6Úpy8z: length mismatch qtl=z vs pta=z
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>assert %(py10)sÚpy10Nr )Ú>)zH%(py5)s
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{%(py5)s = %(py2)s
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{%(py2)s = %(py0)s.sum
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}(%(py3)s)
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} > %(py8)sr Úfinite)rB Úpy2rD rE rG z: no finite values in last ©ú<=)z%(py0)s <= %(py2)sÚmax_diffr: )rB rK z : max_diff=ú.2ez exceeds tolerance=z.0ez (compared z values in last Ú)z
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>assert %(py4)sÚpy4)Ú
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isinstanceÚpdÚSeriesÚto_numpyr? Ú
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@pytest_arÚ_call_reprcompareÚ @py_builtinsÚlocalsÚ_should_repr_global_nameÚ _safereprÚ_format_assertmsgÚAssertionErrorÚ_format_explanationÚmaxr ÚisfiniteÚsumÚfloatÚabs)r@ Ú
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pta_resultr9 r: r; rA Ú @py_assert2Ú @py_assert7Ú @py_assert4Ú @py_format9Ú @py_format11ÚcountÚstartÚq_tailÚp_tailrJ Ú @py_assert1Ú @py_assert6Úq_valsÚp_valsrN Ú @py_format3Ú @py_format5s r2 Ú_verify_last_nrt @ sÖ € ô $.¨j¼"¿)¹)×#DÑ#Dˆ*×
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‰^€Fä”R—V’VœBŸFšF 6¨F¡?Ó 3Ó4Ó5€HØ �yÑ ÷ õ ïé ôˆ8�y÷ ò ïé ÷õ ïé ðˆ8÷ ö ïé ðˆ8÷ ó ïé ÷õ ïé ð�y÷ ö ïé ð�y÷ ÷ ïé ðØ ˆ'�˜X c˜NÐ*=¸iȸ_ð Mܘ“[�MÐ!1°,° ¸qð B÷÷ ö ïé ÷õ r4 c óº • \ rS rSrSrS
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g )Ú TestTrendFIRék uM FIR indicators: SMA, WMA, HMA — deterministic convolution, tight tolerance.c ó‚ • S H9 n[ [ US9n[ R " [ US9n[ X#SSU S3S9 M; g )N©é
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é é2 ©Úlengthr zSMA(rP ©r: r; )r ÚCLOSEÚtaÚSERIESrt ©Úselfr~ ÚqtlrA s r2 Útest_smaÚTestTrendFIR.test_sman ó@ € Û"ˆFÜ”e FÑ+ˆCÜ—&’&œ¨Ñ/ˆCÜ ˜3¨tØ#'¨ x¨qÐ!1ô
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3ò #r4 c ó‚ • S H9 n[ [ US9n[ R " [ US9n[ X#SSU S3S9 M; g )N©rz é é r} r zWMA(rP r )r r€ r� r‚ rt rƒ s r2 Útest_wmaÚTestTrendFIR.test_wmau rˆ r4 c ó‚ • S H9 n[ [ US9n[ R " [ US9n[ X#SSU S3S9 M; g )N)é r‹ r{ r} ç:Œ0âŽyE>zHMA(rP r )r r€ r� r‚ rt rƒ s r2 Útest_hmaÚTestTrendFIR.test_hma| ó@ € Û!ˆFÜ”e FÑ+ˆCÜ—&’&œ¨Ñ/ˆCÜ ˜3¨tØ#'¨ x¨qÐ!1ô
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3ò "r4 u• TRIMA kernel differs: quantalib uses symmetric triangular convolution, pandas-ta delegates to TA-Lib which uses cascaded SMA (SPEC §9.3 known delta)©Úreasonc óh • [ [ SS9n[ R " [ SS9n[ XSSS9 g )Nr‹ r} r z TRIMA(14)r )r
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r€ r� r‚ rt ©r„ r… rA s r2 Ú
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test_trimaÚTestTrendFIR.test_trimaƒ s, € ô ”E "Ñ%ˆÜ�hŠh”v bÑ)ˆÜ�s¨4°{ÓCr4 uZ ALMA sigma/offset defaults differ between quantalib and pandas-ta (SPEC §9.3 known delta)c óh • [ [ SS9n[ R " [ SS9n[ XSSS9 g )Nr‹ r} ç�íµ ÷Æ°>zALMA(14)r )r r€ r� r‚ rt r˜ s r2 Ú test_almaÚTestTrendFIR.test_alma‹ s, € ô ”5 Ñ$ˆÜ�gŠg”f RÑ(ˆÜ�s¨4°zÓBr4 © N©ÚreturnÚNone)Ú__name__Ú
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__module__Ú __qualname__Ú__firstlineno__Ú__doc__r† r� r’ ÚpytestÚmarkÚxfailr™ r� Ú__static_attributes__rŸ r4 r2 rv rv k st † ÙWô3ô3ô3ð ‡[�[×Ñð RÐð SóDóSðDð
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‡[�[×Ñð RÐð SóCóSóCr4 rv c ó6 • \ rS rSrSrSS jrSS jrSS jrSrg) Ú TestTrendIIRé— u IIR indicators: EMA, DEMA, TEMA — recursive convergence.
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With 5000 bars the warmup difference is buried in the past.
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The last 100 bars should match tightly.
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c ó‚ • S H9 n[ [ US9n[ R " [ US9n[ X#SSU S3S9 M; g )Nry r} çH¯¼šò×z>zEMA(rP r )r r€ r� r‚ rt rƒ s r2 Útest_emaÚTestTrendIIR.test_emaž rˆ r4 c ó‚ • S H9 n[ [ US9n[ R " [ US9n[ X#SSU S3S9 M; g )Nry r} r° zDEMA(rP r )r r€ r� r‚ rt rƒ s r2 Ú test_demaÚTestTrendIIR.test_dema¥ ó@ € Û"ˆFÜ”u VÑ,ˆCÜ—'’'œ&¨Ñ0ˆCÜ ˜3¨tØ#(¨¨°Ð!2ô
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4ò #r4 c ó‚ • S H9 n[ [ US9n[ R " [ US9n[ X#SSU S3S9 M; g )NrŠ r} r° zTEMA(rP r )r r€ r� r‚ rt rƒ s r2 Ú test_temaÚTestTrendIIR.test_tema¬ r¶ r4 rŸ Nr ) r£ r¤ r¥ r¦ r§ r± r´ r¸ r« rŸ r4 r2 r r — s † ñô 3ô4÷4r4 r c ón • \ rS rSrSrS
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S jr\R R SS9S
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Sr g ) Ú TestMomentumé¸ z$Momentum: RSI (recursive), ROC, MOM.c ó‚ • S H9 n[ [ US9n[ R " [ US9n[ X#SSU S3S9 M; g)zqRSI is recursive (Wilder smoothing). With 5000 bars, warmup
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convergence difference is negligible in the last 100.)é r‹ é r} rœ zRSI(rP r N)r r€ r� r‚ rt rƒ s r2 Útest_rsiÚTestMomentum.test_rsi» sB € ó "ˆFÜ”e FÑ+ˆCÜ—&’&œ¨Ñ/ˆCÜ ˜3¨tØ#'¨ x¨qÐ!1ô
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3ò "r4 u“ ROC formula differs: quantalib uses absolute difference (close-prev), pandas-ta uses percentage ((close/prev - 1)*100). Known delta per SPEC §9.3.r• c óh • [ [ SS9n[ R " [ SS9n[ XSSS9 g)z¶ROC: quantalib 'Roc' is Rate of Change (Absolute) = close - close[n].
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pandas-ta 'roc' is Rate of Change (Percentage) = ((c/c[n])-1)*100.
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These are fundamentally different indicators.rz r} r° zROC(10)r N)r
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r€ r� r‚ rt r˜ s r2 Útest_rocÚTestMomentum.test_rocÄ s, € ô ”% Ñ#ˆÜ�fŠf”V BÑ'ˆÜ�s¨4°yÓAr4 c ó‚ • S H9 n[ [ US9n[ R " [ US9n[ X#SSU S3S9 M; g )N)é rz r{ r} r zMOM(rP r )r r€ r� r‚ rt rƒ s r2 Útest_momÚTestMomentum.test_momÐ r” r4 rŸ Nr ) r£ r¤ r¥ r¦ r§ rÀ r¨ r© rª rà rÇ r« rŸ r4 r2 r» r» ¸ s< † Ù.ô3ð ‡[�[×Ñð =Ðð >óBó >ðB÷3r4 r» c ó6 • \ rS rSrSrSS jrSS jrSS jrSrg) ÚTestStatisticséÜ zÓSTDDEV, VARIANCE, ZSCORE.
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Note: pandas-ta uses sample stddev (ddof=1), quantalib may use population.
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With 5000 bars and period=20, the difference is ~5% for ddof effect.
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We use relative tolerance where needed.
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US S US 35 GMæ g ) Nry r} r zSTDDEV(ú): no finite in tailr‘ r ú
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): direct=rO õ , pop→sample=õ , sample→pop=)r r€ r� Ústdevr‚ rU r? r_ Ú VERIFY_COUNTr r` ra r¨ Úfailrb rc r ©r„ r~ r… rA Úpta_nprj rk rl rm rJ Úq_fÚp_frN ÚadjustedÚadj_diffÚ adjusted_invÚinv_diffs r2 Ú test_stddevÚTestStatistics.test_stddevä sŸ € Ü"ˆFÜœ vÑ.ˆCÜ—(’(œ6¨&Ñ1ˆCð —\‘\“^ˆFܘ“HˆEܘ˜5¤<Ñ/Ó0ˆEؘ�[ˆFؘF�^ˆFÜ—[’[ Ó(¬2¯;ª;°vÓ+>Ñ>ˆFä�vŠv�f‹~ Ó"Ü— ’ ˜g f XÐ-AÐ BÔCà‘.ˆCØ‘.ˆCô œRŸVšV¤B§F¢F¨3©9Ó$5Ó 6Ó7ˆHؘ4Ó Ùð œRŸWšW V¸©zÑ%:Ó ;Ñ;ˆHÜ œRŸVšV¤B§F¢F¨8©>Ó$:Ó ;Ó<ˆHؘ4Ó Ù𠤧¢¨&°1©*¸Ñ)>Ó!?Ñ?ˆLÜ œRŸVšV¤B§F¢F¨<Ñ+=Ó$>Ó ?Ó@ˆHؘ4Ó Ùä �KŠKؘ&˜ ¨H°S¨>ð : Ø (¨˜~¨_¸XÀc¸NðL÷
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òK #r4 c ó† • S GHº n[ [ US9n[ R " [ US9nUR 5 n[ U5 n[
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test_varianceÚ TestStatistics.test_variance s‡ € Ü"ˆFÜœ5¨Ñ0ˆCÜ—+’+œf¨VÑ4ˆCØ—\‘\“^ˆFܘ“HˆEܘ˜5¤<Ñ/Ó0ˆEؘ�[ˆFؘF�^ˆFÜ—[’[ Ó(¬2¯;ª;°vÓ+>Ñ>ˆFä�vŠv�f‹~ Ó"Ü— ’ ˜i¨ xÐ/CÐ DÔEà‘.ˆCØ‘.ˆCô œRŸVšV¤B§F¢F¨3©9Ó$5Ó 6Ó7ˆHؘ4Ó Ùð ¨°©
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