+ j6 Rt^RIHt^RIt^RIHtHt^RIHt^RIt ^RI t .R,Ot .R-Ot .R.Ot^^K.t^t^ dict Returns a dict suitable for inclusion in dashboard_data.json with Activity tier (from signal density) and Macro tier (from spread state) for each pair, plus descriptive context. This module is read-only — it never writes thresholds. The cutoffs come from data/research/regime_thresholds.json which is calibrated by src/research/regime_classifier_v1.py. If regime_thresholds.json is missing or malformed, compute_regimes() returns a dict with all tiers set to "Unknown" — the dashboard renders a placeholder rather than crashing. ) annotationsN)datetime timedelta)PathQuietLightSteadyActivePeak CompressedSubduedNormalElevatedHigh Dislocationg?zeStrategy firing at top 10% of historical density. Setups occurring multiple times per day on average.zVAbove-average signal density (60-90th percentile of history). Steady stream of setups.zYTypical signal density at historical median (~7 trades / 14d). Standard operating rhythm.zaBelow-average signal density (10-30th percentile). Setups present but less frequent than average.utSignal density in bottom 10% of history. Few setups expected — strategy edge unchanged, frequency is the variable.UnknownuACutoffs not yet calibrated — run regime_classifier_v1.py first.zAbove-average signal density (top 25% of history). Common during BoJ policy shifts, JPY intervention windows, or Fed-BoJ divergence stretches.zYTypical signal density at historical median (~5 trades / 60d). Standard operating regime.z}Signal density in bottom 25% of history (<3 trades / 60d). Common when US and JP central banks are in similar policy stances.zUS-DE 2Y spread heavily out of sync. Composite score in top 10% of history. Spreads moving fast and/or sitting at extreme levels.zgAbove-average spread dislocation (60-90th percentile). Either velocity is high, level is wide, or both.zOTypical macro conditions. Spread moving and sitting in normal historical range.zGBelow-average dislocation. Spreads stable, level near central tendency.zNUS-DE 2Y spread tight or rangebound. Composite score in bottom 10% of history.zPUS-JP 2Y spread showing meaningful moves. Composite score in top 25% of history.zHSpread in typical range with normal movement. Standard operating regime.zqSpread compressed or stable. Bottom 25% of history. Common when both central banks are in similar policy stances.cVfR#\V\4'd\P!V4'dR#\ V4Fwr4W8gK W#,u# VR,#)zuGiven a value, list of ascending cutoffs (length N-1), and list of N tier names (lowest-first), return the tier name.r) isinstancefloatnpisnan enumerate)valuecutoffsnamesics&&& NC:\Users\Administrator\OneDrive\fx_macro_intraday\src\execution\regime_live.py _assign_tierrOsQ }%BHHUOO'" 98O# 9c\V4R, R, R, pVP4'gR#\P!VP RR74# \ dR#i;i)zBLoad regime_thresholds.json if it exists. Returns None on failure.dataresearchzregime_thresholds.jsonNutf-8encoding)rexistsjsonloads read_text Exception) base_pathps& r_load_thresholdsr-\sZ Y& :-0HHA 88::zz!++w+788 s%A A/.A/c(\V4R, R, R, V, pVP4'gR#\P!V4pVPUu.uFqUP 4NK upVn\P !VR,4VR&\P!WB,RR7WB&VRV.,P4PR4PRR7#uupi \dR#i;i) r!rawratesNdatecoerce)errorsTdrop) rr&pdread_csvcolumnslower to_datetime to_numericdropna sort_values reset_indexr*)r+fnamecolr,dfrs&&& r _load_raterBgs Y& 5(72U:A 88:: [[^)+4Aggi4 ^^BvJ/6 --963- '')55f=IItITT5 s%$D%C==A?D=D DDc \WV4p\WV4pVeVfR#\P!R\P!\ VR,P 4VR,P 44\ VR,P 4VR,P 44RR7/4p\P !WuRRR7P4p\P !WvRRR7P4p\P !WVRRR7P4pW,W,, VR&VRR.,PR R 7#) z@Build daily spread = A - B, forward-filled across calendar days.Nr1D)freqleft)onhowinnerspreadTr4) rBr6 DataFrame date_rangeminmaxmergeffillr<r>) r+a_csva_colb_csvb_colabdrrAs &&&&& r_build_spread_seriesrXus9U+A9U+AyAI vr}} AfIMMOQvY]]_- AfIMMOQvY]]_-C 9: ;B 6v.446A 6v.446A !6w / 6 6 8B9ry(BxL vx ! - -4 - 88rc VP4pVR,VR,PV4, VR&V^,p\V4V^,8dR#VR,PVRP 4pVR,PVRP 4pVR,PVRP 4pVR,PVRP 4pVR,V, V, VR&VR,V, V, VR&VR,P 4\VR,P 4,,VR&VPR.R7PR ,pRVR,P4R\VR,4R\VR,4R\VR,4R\VR,4/#) zBCompute the macro composite score series. Returns latest row dict.rJvelocityNlevel_z velocity_z composite)subsetr1r) copyshiftlenilocmeanstdabs LEVEL_WEIGHTr< isoformatr) spread_df velocity_daysrAwarmuplmlsvmvslatests && r_compute_composite_scorerps  B\BxL$6$6}$EEBzN R F 2w! H  67 # ( ( *B H  67 # ' ' )B J  VW % * * ,B J  VW % ) ) +B8 +r1ByM:+r1B|,'++- r)}?P?P?R0RRB{O YY{mY , 1 1" 5FfVn..0eF8,-eF9-.eF<01eF;/0  rc\V4R, R, RVP4 R2, pVP4'g\P!RR7#\ P !VPRR74p\V\4'g\P!RR7#.pVFCpR F:pWe9gK \P!WV,4pVPV4KA KE \P!V4# \dKfi;i \d\P!RR7u#i;i) uLoad entry/close timestamps from the live monitor's trade_history_.json. The live JSON schema records `closed_at` (trade close time), not entry_time. For activity counting purposes we use close time as a proxy for entry time — trades hold 24-52h max so the shift is small relative to the 14d/60d activity windows. Returns pandas Series of timestamps, or empty Series on failure. r!logstrade_history_z.jsonzdatetime64[ns])dtyper#r$) entry_time closed_atentry open_time close_timetime) rr9r&r6Seriesr'r(r)rlistr:appendr*)r+pairr,livetimestradekts&& r_load_live_trade_timesrs Y& 6)nTZZ\N%,PPA 88::yy/001zz!++w+78$%%99#34 4E\:NN584 Q ]yy% 1yy/001s=!AD'4D' -D6D' D$ D'#D$$D''"E  E c\W4pVP'dR#\PP 4pV\P !VR7, p\ W58W48*,P44pV\V43#)uCount trades in the trailing window_days ending NOW, from live JSON only. The backtest CSVs are not consulted here — they're historical artifacts that don't update continuously. For live regime classification we use the live monitor's trade_history_.json as the single source of truth. Returns a tuple (count, total_live_trades): count = trades within window total_live_trades = total trades in live history (for "early sample" detection) )days)r) remptyr6 Timestampnow Timedeltaintsumra)r+r~ window_daysrrcutoff in_windows&&& r_count_trades_in_windowrsl #9 3E {{{ ,,   C 2<<[1 1Fen6;;=>I c%j  rc\V4pR\P!4PRR7RR/R//RR/R//R./p\ V4pVfVR,P R 4^p^p\ VR\4wrVR pV'd RV9dVR,PR 4pV'd\YW;'g.RO\4MR pR VRVR\RVR\PVR4RVRWc8/VR,R&\ VR\4wrR p V'd RV9dVR,PR 4p V 'd\Y;'g^^.\4MR p R V RV R\RV R\PV R4RV RW8/VR,R&R p R pV'dRV9dyVR,PR4pVR,PR^4pV'd@\VRRRR4pVe-\!VV4pV'd\VR,V\"4p R V RVR\$PV R4/VR,R&R pR pV'dRV9dyVR,PR4pVR,PR^<4pV'd@\VRRRR4pVe-\!VV4pV'd\VR,V\&4pR VRVR\(PVR4/VR,R&V#)uCompute Activity + Macro regime tiers for both pairs. Args: base_path: project root Path (where data/ lives) Returns: dict suitable for direct inclusion in dashboard_data.json Activity counts are sourced from the live monitor's trade history JSON (data/logs/trade_history_eurusd.json and trade_history_usdjpy.json) only. Backtest CSVs are not consulted at runtime — those are historical and do not auto-update. When live history is small (early deployment), an "early_sample" flag is set so the dashboard can show a caveat. updated_atseconds)timespeceurusdactivitymacrousdjpyr3Nu>regime_thresholds.json missing — run regime_classifier_v1.pyactivity_cutoffsrtiercountrr description total_live early_samplerizus2y.csvus2yzde2y.csvde2yr]r!zjp2y.csvjp2y)rrrr)rrrrgr-r}rACTIVITY_WINDOW_EUgetrACTIVITY_NAMES_EUACTIVITY_DESCRIPTIONS_EUACTIVITY_WINDOW_UJACTIVITY_NAMES_UJACTIVITY_DESCRIPTIONS_UJrXrpMACRO_NAMES_EUMACRO_DESCRIPTIONS_EUMACRO_NAMES_UJMACRO_DESCRIPTIONS_UJ)r+out thresholdsEU_MIN_TRADES_FOR_RELIABLEUJ_MIN_TRADES_FOR_RELIABLEeu_counteu_totaleu_act_cutoffs eu_act_tieruj_countuj_totaluj_act_cutoffs uj_act_tier eu_macro_tier eu_macro_dataeu_macro_cutoffseu_velocity_daysrJ uj_macro_tier uj_macro_datauj_macro_cutoffsuj_velocity_dayss& rcompute_regimesrs<YI hlln.. .B:r7B/:r7B/"  C"),J H ]^"$!"1HFXYHNh*,#H-112DE x)G)G!CM*1HFXYHNh*,#H-112DE x)A)AAq6CTU( +155k2F>!CM*MMh*,'155i@'155orJ ))ZU[\F! 8AQ R  $0%k24Dn%VM   -11-DCM'MMh*,'155i@'155orJ ))ZU[\F! 8AQ R  $0%k24Dn%VM   -11-DCM' Jr__main__)indentdefault)rrrr r )rrr ) <Z)r r r rr)r r r )*__doc__ __future__rr'rrpathlibrnumpyrpandasr6rrACTIVITY_PCTS_EUACTIVITY_PCTS_UJrrrrrfrrrrrr-rBrXrprrr__name____file__resolveparentsbaseresultprintdumpsstrrrrs*# (C1$HU0  u f i q E Q  _ i N Q \B i ah [  j b L [    9 01B!*iZ z > ! ! # + +A .D T "F $**VAs 34r