Index: backend/docs/P6_Advanced_Reports.txt
===================================================================
--- backend/docs/P6_Advanced_Reports.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ backend/docs/P6_Advanced_Reports.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,702 @@
+= Напредни извештаи од базата (SQL, складирани процедури и релациона алгебра)
+
+=== 1. Детален годишен извештај за финансиска резилиентност, стабилност на приходи и буџетски притисок по корисник
+
+==== SQL
+{{{
+SET search_path TO trekr;
+
+WITH params AS (
+    SELECT 2026::int AS report_year
+),
+months AS (
+    SELECT generate_series(1, 12) AS month_no
+),
+finance_base AS (
+    SELECT
+        fu.user_id,
+        u.username,
+        u.email,
+        COALESCE(fu.spending_budget, 0) AS spending_budget,
+        COALESCE(fu.saving_budget, 0) AS saving_budget,
+        COALESCE(fu.investing_budget, 0) AS investing_budget,
+        COALESCE(fu.donation_budget, 0) AS donation_budget,
+        COALESCE(fu.credit, 0) AS credit
+    FROM finance_users fu
+    JOIN users u ON u.user_id = fu.user_id
+),
+monthly_income AS (
+    SELECT
+        fb.user_id,
+        m.month_no,
+        COALESCE(SUM(i.amount), 0) AS month_income
+    FROM finance_base fb
+    CROSS JOIN months m
+    LEFT JOIN incomes i
+        ON i.user_id = fb.user_id
+       AND EXTRACT(YEAR FROM i.date)::int = (SELECT report_year FROM params)
+       AND EXTRACT(MONTH FROM i.date)::int = m.month_no
+    GROUP BY fb.user_id, m.month_no
+),
+monthly_income_ranked AS (
+    SELECT
+        mi.*,
+        DENSE_RANK() OVER (PARTITION BY mi.user_id ORDER BY mi.month_income DESC, mi.month_no ASC) AS best_month_rank,
+        DENSE_RANK() OVER (PARTITION BY mi.user_id ORDER BY mi.month_income ASC, mi.month_no ASC) AS worst_month_rank
+    FROM monthly_income mi
+),
+annual_income AS (
+    SELECT
+        user_id,
+        SUM(month_income) AS total_income,
+        AVG(month_income) AS avg_monthly_income,
+        STDDEV_SAMP(month_income) AS income_stddev,
+        MAX(month_income) AS best_month_income,
+        MIN(month_income) AS worst_month_income,
+        COUNT(*) FILTER (WHERE month_income > 0) AS active_income_months
+    FROM monthly_income
+    GROUP BY user_id
+),
+best_worst_months AS (
+    SELECT
+        user_id,
+        MAX(month_no) FILTER (WHERE best_month_rank = 1) AS best_month_no,
+        MAX(month_no) FILTER (WHERE worst_month_rank = 1) AS worst_month_no
+    FROM monthly_income_ranked
+    GROUP BY user_id
+)
+SELECT
+    fb.user_id,
+    fb.username,
+    fb.email,
+    (fb.spending_budget + fb.saving_budget + fb.investing_budget + fb.donation_budget) * 12 AS planned_annual_budget,
+    ai.total_income AS actual_annual_income,
+    ai.avg_monthly_income,
+    ai.active_income_months,
+    ai.best_month_income,
+    ai.worst_month_income,
+    bwm.best_month_no,
+    bwm.worst_month_no,
+    ROUND(
+        (ai.income_stddev / NULLIF(ai.avg_monthly_income, 0))::numeric,
+        4
+    ) AS income_volatility_cv,
+    ROUND(
+        (ai.total_income - (fb.spending_budget * 12))::numeric,
+        2
+    ) AS annual_free_cash_after_spending,
+    ROUND(
+        ((fb.spending_budget * 12) / NULLIF(ai.total_income, 0))::numeric,
+        4
+    ) AS spending_pressure_ratio,
+    ROUND(
+        (fb.credit / NULLIF(ai.total_income, 0))::numeric,
+        4
+    ) AS leverage_ratio,
+    DENSE_RANK() OVER (
+        ORDER BY
+            (ai.total_income - (fb.spending_budget * 12)) DESC,
+            ((fb.spending_budget * 12) / NULLIF(ai.total_income, 0)) ASC,
+            fb.user_id ASC
+    ) AS finance_resilience_rank
+FROM finance_base fb
+JOIN annual_income ai ON ai.user_id = fb.user_id
+JOIN best_worst_months bwm ON bwm.user_id = fb.user_id
+ORDER BY finance_resilience_rank, fb.user_id;
+}}}
+
+==== Релациона Алгебра
+{{{
+FB <- pi_{fu.user_id, u.username, u.email,
+          COALESCE(fu.spending_budget,0)->spending_budget,
+          COALESCE(fu.saving_budget,0)->saving_budget,
+          COALESCE(fu.investing_budget,0)->investing_budget,
+          COALESCE(fu.donation_budget,0)->donation_budget,
+          COALESCE(fu.credit,0)->credit}
+      (finance_users fu bowtie_{fu.user_id = u.user_id} users u)
+
+FBM <- FB x M
+IY <- sigma_{YEAR(i.date)=Y}(incomes i)
+MI0 <- FBM leftouterjoin_{FBM.user_id = i.user_id AND FBM.month_no = MONTH(i.date)} IY
+MI <- gamma_{user_id, month_no;
+             SUM(COALESCE(i.amount,0))->month_income}(MI0)
+
+MIR <- omega_{PARTITION BY user_id ORDER BY month_income DESC, month_no ASC;
+              DENSE_RANK()->best_month_rank,
+              DENSE_RANK(PARTITION BY user_id ORDER BY month_income ASC, month_no ASC)->worst_month_rank}(MI)
+
+AI <- gamma_{user_id;
+             SUM(month_income)->total_income,
+             AVG(month_income)->avg_monthly_income,
+             STDDEV_SAMP(month_income)->income_stddev,
+             MAX(month_income)->best_month_income,
+             MIN(month_income)->worst_month_income,
+             COUNT_IF(month_income>0)->active_income_months}(MI)
+
+BWM <- gamma_{user_id;
+              MAX_IF(month_no, best_month_rank=1)->best_month_no,
+              MAX_IF(month_no, worst_month_rank=1)->worst_month_no}(MIR)
+
+R0 <- FB bowtie_{FB.user_id=AI.user_id} AI bowtie_{FB.user_id=BWM.user_id} BWM
+R1 <- alpha_{(spending_budget+saving_budget+investing_budget+donation_budget)*12->planned_annual_budget,
+             total_income->actual_annual_income,
+             income_stddev/NULLIF(avg_monthly_income,0)->income_volatility_cv,
+             total_income-(spending_budget*12)->annual_free_cash_after_spending,
+             (spending_budget*12)/NULLIF(total_income,0)->spending_pressure_ratio,
+             credit/NULLIF(total_income,0)->leverage_ratio}(R0)
+R  <- omega_{ORDER BY annual_free_cash_after_spending DESC,
+                    spending_pressure_ratio ASC,
+                    user_id ASC;
+             DENSE_RANK()->finance_resilience_rank}(R1)
+}}}
+
+=== 2. Детален годишен извештај за конзистентност на тренинг, оптоварување и тренд на перформанс
+
+==== SQL
+{{{
+SET search_path TO trekr;
+
+WITH params AS (
+    SELECT 2026::int AS report_year
+),
+months AS (
+    SELECT generate_series(1, 12) AS month_no
+),
+training_base AS (
+    SELECT
+        tu.user_id,
+        u.username,
+        u.email,
+        tu.gender,
+        tu.age,
+        tu.weight
+    FROM training_users tu
+    JOIN users u ON u.user_id = tu.user_id
+),
+monthly_sessions AS (
+    SELECT
+        tb.user_id,
+        m.month_no,
+        COALESCE(COUNT(ts.training_id), 0) AS sessions_count,
+        COALESCE(SUM(ts.duration), 0) AS total_duration_minutes,
+        COALESCE(SUM(ts.calories), 0) AS total_calories,
+        COALESCE(AVG(ts.duration), 0) AS avg_session_duration,
+        COALESCE(AVG(ts.calories), 0) AS avg_session_calories
+    FROM training_base tb
+    CROSS JOIN months m
+    LEFT JOIN training_sessions ts
+        ON ts.training_user_id = tb.user_id
+       AND EXTRACT(YEAR FROM ts.date)::int = (SELECT report_year FROM params)
+       AND EXTRACT(MONTH FROM ts.date)::int = m.month_no
+    GROUP BY tb.user_id, m.month_no
+),
+monthly_ranked AS (
+    SELECT
+        ms.*,
+        DENSE_RANK() OVER (PARTITION BY ms.user_id ORDER BY ms.total_calories DESC, ms.month_no ASC) AS peak_calorie_month_rank,
+        DENSE_RANK() OVER (PARTITION BY ms.user_id ORDER BY ms.sessions_count DESC, ms.month_no ASC) AS peak_sessions_month_rank
+    FROM monthly_sessions ms
+),
+active_month_streaks AS (
+    SELECT
+        user_id,
+        month_no,
+        month_no - ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY month_no) AS grp
+    FROM monthly_sessions
+    WHERE sessions_count > 0
+),
+longest_streak AS (
+    SELECT
+        user_id,
+        MAX(streak_len) AS longest_active_month_streak
+    FROM (
+        SELECT user_id, grp, COUNT(*) AS streak_len
+        FROM active_month_streaks
+        GROUP BY user_id, grp
+    ) s
+    GROUP BY user_id
+),
+annual_training AS (
+    SELECT
+        user_id,
+        SUM(sessions_count) AS annual_sessions,
+        SUM(total_duration_minutes) AS annual_duration_minutes,
+        SUM(total_calories) AS annual_calories,
+        AVG(total_duration_minutes) AS avg_monthly_duration,
+        AVG(total_calories) AS avg_monthly_calories,
+        COUNT(*) FILTER (WHERE sessions_count > 0) AS active_months,
+        REGR_SLOPE(total_calories::numeric, month_no::numeric) AS calories_trend_slope,
+        REGR_SLOPE(total_duration_minutes::numeric, month_no::numeric) AS duration_trend_slope
+    FROM monthly_sessions
+    GROUP BY user_id
+),
+peak_months AS (
+    SELECT
+        user_id,
+        MAX(month_no) FILTER (WHERE peak_calorie_month_rank = 1) AS peak_calorie_month_no,
+        MAX(month_no) FILTER (WHERE peak_sessions_month_rank = 1) AS peak_sessions_month_no
+    FROM monthly_ranked
+    GROUP BY user_id
+)
+SELECT
+    tb.user_id,
+    tb.username,
+    tb.email,
+    tb.gender,
+    tb.age,
+    tb.weight,
+    at.annual_sessions,
+    ROUND(at.annual_duration_minutes::numeric, 2) AS annual_duration_minutes,
+    ROUND(at.annual_calories::numeric, 2) AS annual_calories,
+    at.active_months,
+    ROUND((at.active_months / 12.0)::numeric, 4) AS consistency_ratio,
+    COALESCE(ls.longest_active_month_streak, 0) AS longest_active_month_streak,
+    pm.peak_calorie_month_no,
+    pm.peak_sessions_month_no,
+    ROUND(COALESCE(at.calories_trend_slope, 0)::numeric, 4) AS calories_trend_slope,
+    ROUND(COALESCE(at.duration_trend_slope, 0)::numeric, 4) AS duration_trend_slope,
+    DENSE_RANK() OVER (
+        ORDER BY
+            at.annual_calories DESC,
+            at.active_months DESC,
+            COALESCE(ls.longest_active_month_streak, 0) DESC,
+            tb.user_id ASC
+    ) AS training_annual_rank
+FROM training_base tb
+JOIN annual_training at ON at.user_id = tb.user_id
+JOIN peak_months pm ON pm.user_id = tb.user_id
+LEFT JOIN longest_streak ls ON ls.user_id = tb.user_id
+ORDER BY training_annual_rank, tb.user_id;
+}}}
+
+==== Релациона Алгебра
+{{{
+TB <- pi_{tu.user_id, u.username, u.email, tu.gender, tu.age, tu.weight}
+      (training_users tu bowtie_{tu.user_id = u.user_id} users u)
+
+TBM <- TB x M
+TSY <- sigma_{YEAR(ts.date)=Y}(training_sessions ts)
+MS0 <- TBM leftouterjoin_{TBM.user_id = ts.training_user_id AND TBM.month_no = MONTH(ts.date)} TSY
+MS <- gamma_{user_id, month_no;
+             COUNT(ts.training_id)->sessions_count,
+             SUM(COALESCE(ts.duration,0))->total_duration_minutes,
+             SUM(COALESCE(ts.calories,0))->total_calories,
+             AVG(COALESCE(ts.duration,0))->avg_session_duration,
+             AVG(COALESCE(ts.calories,0))->avg_session_calories}(MS0)
+
+MR <- omega_{PARTITION BY user_id ORDER BY total_calories DESC, month_no ASC;
+             DENSE_RANK()->peak_calorie_month_rank,
+             DENSE_RANK(PARTITION BY user_id ORDER BY sessions_count DESC, month_no ASC)->peak_sessions_month_rank}(MS)
+
+AMS <- sigma_{sessions_count>0}(MS)
+AMS1 <- omega_{PARTITION BY user_id ORDER BY month_no;
+               ROW_NUMBER()->rn}(AMS)
+AMS2 <- alpha_{month_no - rn -> grp}(AMS1)
+LS0 <- gamma_{user_id, grp; COUNT(*)->streak_len}(AMS2)
+LS  <- gamma_{user_id; MAX(streak_len)->longest_active_month_streak}(LS0)
+
+AT <- gamma_{user_id;
+             SUM(sessions_count)->annual_sessions,
+             SUM(total_duration_minutes)->annual_duration_minutes,
+             SUM(total_calories)->annual_calories,
+             AVG(total_duration_minutes)->avg_monthly_duration,
+             AVG(total_calories)->avg_monthly_calories,
+             COUNT_IF(sessions_count>0)->active_months,
+             REGR_SLOPE(total_calories, month_no)->calories_trend_slope,
+             REGR_SLOPE(total_duration_minutes, month_no)->duration_trend_slope}(MS)
+
+PM <- gamma_{user_id;
+             MAX_IF(month_no, peak_calorie_month_rank=1)->peak_calorie_month_no,
+             MAX_IF(month_no, peak_sessions_month_rank=1)->peak_sessions_month_no}(MR)
+
+R0 <- TB bowtie_{TB.user_id=AT.user_id} AT
+         bowtie_{TB.user_id=PM.user_id} PM
+         leftouterjoin_{TB.user_id=LS.user_id} LS
+R1 <- alpha_{active_months/12.0->consistency_ratio,
+             COALESCE(longest_active_month_streak,0)->longest_active_month_streak_nz,
+             COALESCE(calories_trend_slope,0)->calories_trend_slope_nz,
+             COALESCE(duration_trend_slope,0)->duration_trend_slope_nz}(R0)
+R  <- omega_{ORDER BY annual_calories DESC,
+                    active_months DESC,
+                    longest_active_month_streak_nz DESC,
+                    user_id ASC;
+             DENSE_RANK()->training_annual_rank}(R1)
+}}}
+
+=== 3. Детален годишен извештај за дисциплина, квалитет на завршување и однесување преку streaks
+
+==== SQL
+{{{
+SET search_path TO trekr;
+
+WITH params AS (
+    SELECT 2026::int AS report_year
+),
+discipline_base AS (
+    SELECT
+        du.user_id,
+        u.username,
+        u.email
+    FROM discipline_users du
+    JOIN users u ON u.user_id = du.user_id
+),
+task_mix AS (
+    SELECT
+        COALESCE(t.discipline_user_id, c.user_id) AS user_id,
+        COUNT(*) AS total_tasks_defined,
+        COUNT(*) FILTER (WHERE t.custom_tracking_id IS NULL) AS core_tasks,
+        COUNT(*) FILTER (WHERE t.custom_tracking_id IS NOT NULL) AS custom_tasks,
+        COUNT(DISTINCT COALESCE(t.custom_tracking_id::text, 'core')) AS task_category_span
+    FROM tasks t
+    LEFT JOIN custom_tracking_categories c
+        ON c.custom_tracking_id = t.custom_tracking_id
+    WHERE t.discipline_user_id IS NOT NULL
+       OR t.custom_tracking_id IS NOT NULL
+    GROUP BY COALESCE(t.discipline_user_id, c.user_id)
+),
+annual_daily_completion AS (
+    SELECT
+        dc.user_id,
+        dc.date,
+        COALESCE(dc.procent, 0) AS procent,
+        CASE WHEN COALESCE(dc.procent, 0) >= 80 THEN 1 ELSE 0 END AS strong_day
+    FROM daily_completion dc
+    WHERE EXTRACT(YEAR FROM dc.date)::int = (SELECT report_year FROM params)
+),
+daily_completion_stats AS (
+    SELECT
+        adc.user_id,
+        COUNT(*) AS tracked_days,
+        AVG(adc.procent) AS avg_completion_percent,
+        PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY adc.procent) AS median_completion_percent,
+        COUNT(*) FILTER (WHERE adc.procent = 100) AS perfect_days,
+        COUNT(*) FILTER (WHERE adc.procent >= 80) AS strong_days,
+        STDDEV_SAMP(adc.procent) AS completion_variability
+    FROM annual_daily_completion adc
+    GROUP BY adc.user_id
+),
+strong_day_streaks AS (
+    SELECT
+        user_id,
+        date,
+        date - (ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY date))::int AS grp
+    FROM annual_daily_completion
+    WHERE strong_day = 1
+),
+longest_strong_streak AS (
+    SELECT
+        user_id,
+        MAX(streak_len) AS longest_strong_day_streak
+    FROM (
+        SELECT user_id, grp, COUNT(*) AS streak_len
+        FROM strong_day_streaks
+        GROUP BY user_id, grp
+    ) s
+    GROUP BY user_id
+),
+annual_task_execution AS (
+    SELECT
+        dc.user_id,
+        COUNT(tdc.task_id) AS completed_task_events
+    FROM daily_completion dc
+    LEFT JOIN task_daily_completion tdc
+        ON tdc.daily_completion_id = dc.daily_completion_id
+    WHERE EXTRACT(YEAR FROM dc.date)::int = (SELECT report_year FROM params)
+    GROUP BY dc.user_id
+)
+SELECT
+    db.user_id,
+    db.username,
+    db.email,
+    COALESCE(tm.total_tasks_defined, 0) AS total_tasks_defined,
+    COALESCE(tm.core_tasks, 0) AS core_tasks,
+    COALESCE(tm.custom_tasks, 0) AS custom_tasks,
+    COALESCE(tm.task_category_span, 0) AS task_category_span,
+    COALESCE(dcs.tracked_days, 0) AS tracked_days,
+    ROUND(COALESCE(dcs.avg_completion_percent, 0)::numeric, 2) AS avg_completion_percent,
+    ROUND(COALESCE(dcs.median_completion_percent, 0)::numeric, 2) AS median_completion_percent,
+    COALESCE(dcs.perfect_days, 0) AS perfect_days,
+    COALESCE(dcs.strong_days, 0) AS strong_days,
+    ROUND(COALESCE(dcs.completion_variability, 0)::numeric, 4) AS completion_variability,
+    COALESCE(ate.completed_task_events, 0) AS completed_task_events,
+    COALESCE(lss.longest_strong_day_streak, 0) AS longest_strong_day_streak,
+    ROUND(
+        COALESCE((COALESCE(dcs.strong_days, 0) / NULLIF(COALESCE(dcs.tracked_days, 0), 0)::numeric), 0),
+        4
+    ) AS strong_day_ratio,
+    ROUND(
+        (
+            COALESCE(dcs.avg_completion_percent, 0) * 0.45
+            + COALESCE(lss.longest_strong_day_streak, 0) * 2.00
+            + COALESCE(ate.completed_task_events, 0) * 0.35
+        )::numeric,
+        2
+    ) AS discipline_composite_score,
+    DENSE_RANK() OVER (
+        ORDER BY
+            (
+                COALESCE(dcs.avg_completion_percent, 0) * 0.45
+                + COALESCE(lss.longest_strong_day_streak, 0) * 2.00
+                + COALESCE(ate.completed_task_events, 0) * 0.35
+            ) DESC,
+            db.user_id ASC
+    ) AS discipline_annual_rank
+FROM discipline_base db
+LEFT JOIN task_mix tm ON tm.user_id = db.user_id
+LEFT JOIN daily_completion_stats dcs ON dcs.user_id = db.user_id
+LEFT JOIN annual_task_execution ate ON ate.user_id = db.user_id
+LEFT JOIN longest_strong_streak lss ON lss.user_id = db.user_id
+ORDER BY discipline_annual_rank, db.user_id;
+}}}
+
+==== Релациона Алгебра
+{{{
+DB <- pi_{du.user_id, u.username, u.email}
+      (discipline_users du bowtie_{du.user_id = u.user_id} users u)
+
+TC <- tasks t leftouterjoin_{t.custom_tracking_id = c.custom_tracking_id} custom_tracking_categories c
+TM0 <- alpha_{COALESCE(t.discipline_user_id, c.user_id)->owner_user_id}(TC)
+TM1 <- sigma_{t.discipline_user_id IS NOT NULL OR t.custom_tracking_id IS NOT NULL}(TM0)
+TM <- gamma_{owner_user_id;
+             COUNT(*)->total_tasks_defined,
+             COUNT_IF(t.custom_tracking_id IS NULL)->core_tasks,
+             COUNT_IF(t.custom_tracking_id IS NOT NULL)->custom_tasks,
+             COUNT_DISTINCT(COALESCE(t.custom_tracking_id,'core'))->task_category_span}(TM1)
+
+ADC0 <- sigma_{YEAR(dc.date)=Y}(daily_completion dc)
+ADC <- alpha_{COALESCE(dc.procent,0)->procent,
+              CASE(procent>=80,1,0)->strong_day}(ADC0)
+
+DCS <- gamma_{user_id;
+              COUNT(*)->tracked_days,
+              AVG(procent)->avg_completion_percent,
+              PERCENTILE_CONT_0_5(procent)->median_completion_percent,
+              COUNT_IF(procent=100)->perfect_days,
+              COUNT_IF(procent>=80)->strong_days,
+              STDDEV_SAMP(procent)->completion_variability}(ADC)
+
+SDS0 <- sigma_{strong_day=1}(ADC)
+SDS1 <- omega_{PARTITION BY user_id ORDER BY date; ROW_NUMBER()->rn}(SDS0)
+SDS2 <- alpha_{date - rn -> grp}(SDS1)
+LSS0 <- gamma_{user_id, grp; COUNT(*)->streak_len}(SDS2)
+LSS  <- gamma_{user_id; MAX(streak_len)->longest_strong_day_streak}(LSS0)
+
+ATE0 <- ADC0 leftouterjoin_{ADC0.daily_completion_id = tdc.daily_completion_id} task_daily_completion tdc
+ATE  <- gamma_{ADC0.user_id; COUNT(tdc.task_id)->completed_task_events}(ATE0)
+
+R0 <- DB
+      leftouterjoin_{DB.user_id = TM.owner_user_id} TM
+      leftouterjoin_{DB.user_id = DCS.user_id} DCS
+      leftouterjoin_{DB.user_id = ATE.user_id} ATE
+      leftouterjoin_{DB.user_id = LSS.user_id} LSS
+R1 <- alpha_{COALESCE(total_tasks_defined,0)->total_tasks_defined_nz,
+             COALESCE(core_tasks,0)->core_tasks_nz,
+             COALESCE(custom_tasks,0)->custom_tasks_nz,
+             COALESCE(task_category_span,0)->task_category_span_nz,
+             COALESCE(tracked_days,0)->tracked_days_nz,
+             COALESCE(avg_completion_percent,0)->avg_completion_percent_nz,
+             COALESCE(median_completion_percent,0)->median_completion_percent_nz,
+             COALESCE(perfect_days,0)->perfect_days_nz,
+             COALESCE(strong_days,0)->strong_days_nz,
+             COALESCE(completion_variability,0)->completion_variability_nz,
+             COALESCE(completed_task_events,0)->completed_task_events_nz,
+             COALESCE(longest_strong_day_streak,0)->longest_strong_day_streak_nz,
+             COALESCE(strong_days/NULLIF(tracked_days,0),0)->strong_day_ratio,
+             (COALESCE(avg_completion_percent,0)*0.45 +
+              COALESCE(longest_strong_day_streak,0)*2.00 +
+              COALESCE(completed_task_events,0)*0.35)->discipline_composite_score}(R0)
+R  <- omega_{ORDER BY discipline_composite_score DESC, user_id ASC;
+             DENSE_RANK()->discipline_annual_rank}(R1)
+}}}
+
+=== 4. Детален годишен извештај за инвестициска диверзификација, концентрација и темпо на вложување
+
+==== SQL
+{{{
+SET search_path TO trekr;
+
+WITH params AS (
+    SELECT 2026::int AS report_year
+),
+months AS (
+    SELECT generate_series(1, 12) AS month_no
+),
+investor_base AS (
+    SELECT
+        iu.user_id,
+        u.username,
+        u.email
+    FROM investor_users iu
+    JOIN users u ON u.user_id = iu.user_id
+),
+annual_asset_lots AS (
+    SELECT
+        a.user_id,
+        a.ticker_symbol,
+        COALESCE(a.quantity, 0) AS quantity,
+        COALESCE(a.buy_price, 0) AS buy_price,
+        COALESCE(a.quantity, 0) * COALESCE(a.buy_price, 0) AS invested_amount,
+        a.buy_date
+    FROM assets a
+    WHERE EXTRACT(YEAR FROM a.buy_date)::int = (SELECT report_year FROM params)
+),
+ticker_rollup AS (
+    SELECT
+        aal.user_id,
+        aal.ticker_symbol,
+        SUM(aal.quantity) AS total_quantity,
+        SUM(aal.invested_amount) AS total_invested_amount,
+        COUNT(*) AS lot_count,
+        MIN(aal.buy_date) AS first_buy_date,
+        MAX(aal.buy_date) AS last_buy_date
+    FROM annual_asset_lots aal
+    GROUP BY aal.user_id, aal.ticker_symbol
+),
+portfolio_totals AS (
+    SELECT
+        user_id,
+        SUM(total_invested_amount) AS annual_total_invested,
+        SUM(lot_count) AS annual_lot_count,
+        COUNT(*) AS distinct_tickers
+    FROM ticker_rollup
+    GROUP BY user_id
+),
+weights AS (
+    SELECT
+        tr.user_id,
+        tr.ticker_symbol,
+        tr.total_invested_amount,
+        pt.annual_total_invested,
+        (tr.total_invested_amount / NULLIF(pt.annual_total_invested, 0)) AS position_weight,
+        DENSE_RANK() OVER (
+            PARTITION BY tr.user_id
+            ORDER BY tr.total_invested_amount DESC, tr.ticker_symbol ASC
+        ) AS position_rank
+    FROM ticker_rollup tr
+    JOIN portfolio_totals pt ON pt.user_id = tr.user_id
+),
+concentration AS (
+    SELECT
+        user_id,
+        SUM(position_weight * position_weight) AS hhi_concentration,
+        MAX(position_weight) AS top_position_weight,
+        MAX(ticker_symbol) FILTER (WHERE position_rank = 1) AS top_ticker
+    FROM weights
+    GROUP BY user_id
+),
+monthly_investment AS (
+    SELECT
+        ib.user_id,
+        m.month_no,
+        COALESCE(SUM(a.quantity * a.buy_price), 0) AS monthly_invested_amount
+    FROM investor_base ib
+    CROSS JOIN months m
+    LEFT JOIN assets a
+        ON a.user_id = ib.user_id
+       AND EXTRACT(YEAR FROM a.buy_date)::int = (SELECT report_year FROM params)
+       AND EXTRACT(MONTH FROM a.buy_date)::int = m.month_no
+    GROUP BY ib.user_id, m.month_no
+),
+monthly_investment_stats AS (
+    SELECT
+        user_id,
+        AVG(monthly_invested_amount) AS avg_monthly_contribution,
+        STDDEV_SAMP(monthly_invested_amount) AS contribution_stddev,
+        COUNT(*) FILTER (WHERE monthly_invested_amount > 0) AS active_investing_months
+    FROM monthly_investment
+    GROUP BY user_id
+)
+SELECT
+    ib.user_id,
+    ib.username,
+    ib.email,
+    COALESCE(pt.annual_total_invested, 0) AS annual_total_invested,
+    COALESCE(pt.annual_lot_count, 0) AS annual_lot_count,
+    COALESCE(pt.distinct_tickers, 0) AS distinct_tickers,
+    ROUND(COALESCE(ms.avg_monthly_contribution, 0)::numeric, 2) AS avg_monthly_contribution,
+    COALESCE(ms.active_investing_months, 0) AS active_investing_months,
+    ROUND((COALESCE(ms.active_investing_months, 0) / 12.0)::numeric, 4) AS activity_ratio,
+    ROUND(COALESCE(c.hhi_concentration, 0)::numeric, 4) AS hhi_concentration,
+    ROUND((1 - COALESCE(c.hhi_concentration, 1))::numeric, 4) AS diversification_index,
+    ROUND(COALESCE(c.top_position_weight, 0)::numeric, 4) AS top_position_weight,
+    c.top_ticker,
+    ROUND((COALESCE(ms.contribution_stddev, 0) / NULLIF(ms.avg_monthly_contribution, 0))::numeric, 4) AS contribution_volatility_cv,
+    DENSE_RANK() OVER (
+        ORDER BY
+            (1 - COALESCE(c.hhi_concentration, 1)) DESC,
+            COALESCE(pt.annual_total_invested, 0) DESC,
+            COALESCE(ms.active_investing_months, 0) DESC,
+            ib.user_id ASC
+    ) AS investing_annual_rank
+FROM investor_base ib
+LEFT JOIN portfolio_totals pt ON pt.user_id = ib.user_id
+LEFT JOIN concentration c ON c.user_id = ib.user_id
+LEFT JOIN monthly_investment_stats ms ON ms.user_id = ib.user_id
+ORDER BY investing_annual_rank, ib.user_id;
+}}}
+
+==== Релациона Алгебра
+{{{
+IB <- pi_{iu.user_id, u.username, u.email}
+      (investor_users iu bowtie_{iu.user_id = u.user_id} users u)
+
+AAL <- pi_{a.user_id, a.ticker_symbol,
+           COALESCE(a.quantity,0)->quantity,
+           COALESCE(a.buy_price,0)->buy_price,
+           COALESCE(a.quantity,0)*COALESCE(a.buy_price,0)->invested_amount,
+           a.buy_date}
+       (sigma_{YEAR(a.buy_date)=Y}(assets a))
+
+TR <- gamma_{user_id, ticker_symbol;
+             SUM(quantity)->total_quantity,
+             SUM(invested_amount)->total_invested_amount,
+             COUNT(*)->lot_count,
+             MIN(buy_date)->first_buy_date,
+             MAX(buy_date)->last_buy_date}(AAL)
+
+PT <- gamma_{user_id;
+             SUM(total_invested_amount)->annual_total_invested,
+             SUM(lot_count)->annual_lot_count,
+             COUNT(*)->distinct_tickers}(TR)
+
+W0 <- TR bowtie_{TR.user_id = PT.user_id} PT
+W1 <- alpha_{total_invested_amount/NULLIF(annual_total_invested,0)->position_weight}(W0)
+W  <- omega_{PARTITION BY user_id ORDER BY total_invested_amount DESC, ticker_symbol ASC;
+             DENSE_RANK()->position_rank}(W1)
+
+C <- gamma_{user_id;
+            SUM(position_weight*position_weight)->hhi_concentration,
+            MAX(position_weight)->top_position_weight,
+            MAX_IF(ticker_symbol, position_rank=1)->top_ticker}(W)
+
+IBM <- IB x M
+AY  <- sigma_{YEAR(a.buy_date)=Y}(assets a)
+MI0 <- IBM leftouterjoin_{IBM.user_id=a.user_id AND IBM.month_no=MONTH(a.buy_date)} AY
+MI  <- gamma_{user_id, month_no;
+              SUM(COALESCE(a.quantity,0)*COALESCE(a.buy_price,0))->monthly_invested_amount}(MI0)
+MS  <- gamma_{user_id;
+              AVG(monthly_invested_amount)->avg_monthly_contribution,
+              STDDEV_SAMP(monthly_invested_amount)->contribution_stddev,
+              COUNT_IF(monthly_invested_amount>0)->active_investing_months}(MI)
+
+R0 <- IB
+      leftouterjoin_{IB.user_id=PT.user_id} PT
+      leftouterjoin_{IB.user_id=C.user_id} C
+      leftouterjoin_{IB.user_id=MS.user_id} MS
+R1 <- alpha_{COALESCE(annual_total_invested,0)->annual_total_invested_nz,
+             COALESCE(annual_lot_count,0)->annual_lot_count_nz,
+             COALESCE(distinct_tickers,0)->distinct_tickers_nz,
+             COALESCE(avg_monthly_contribution,0)->avg_monthly_contribution_nz,
+             COALESCE(active_investing_months,0)->active_investing_months_nz,
+             COALESCE(active_investing_months,0)/12.0->activity_ratio,
+             COALESCE(hhi_concentration,0)->hhi_concentration_nz,
+             1-COALESCE(hhi_concentration,1)->diversification_index,
+             COALESCE(top_position_weight,0)->top_position_weight_nz,
+             COALESCE(contribution_stddev/NULLIF(avg_monthly_contribution,0),0)->contribution_volatility_cv}(R0)
+R  <- omega_{ORDER BY diversification_index DESC,
+                    annual_total_invested_nz DESC,
+                    active_investing_months_nz DESC,
+                    user_id ASC;
+             DENSE_RANK()->investing_annual_rank}(R1)
+}}}
+
Index: db-scripts/p6-relational-algebra/p6_q1_annual_finance_budget_resilience_ra.txt
===================================================================
--- db-scripts/p6-relational-algebra/p6_q1_annual_finance_budget_resilience_ra.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ db-scripts/p6-relational-algebra/p6_q1_annual_finance_budget_resilience_ra.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,42 @@
+FB <- pi_{fu.user_id, u.username, u.email,
+          COALESCE(fu.spending_budget,0)->spending_budget,
+          COALESCE(fu.saving_budget,0)->saving_budget,
+          COALESCE(fu.investing_budget,0)->investing_budget,
+          COALESCE(fu.donation_budget,0)->donation_budget,
+          COALESCE(fu.credit,0)->credit}
+      (finance_users fu bowtie_{fu.user_id = u.user_id} users u)
+
+FBM <- FB x M
+IY <- sigma_{YEAR(i.date)=Y}(incomes i)
+MI0 <- FBM leftouterjoin_{FBM.user_id = i.user_id AND FBM.month_no = MONTH(i.date)} IY
+MI <- gamma_{user_id, month_no;
+             SUM(COALESCE(i.amount,0))->month_income}(MI0)
+
+MIR <- omega_{PARTITION BY user_id ORDER BY month_income DESC, month_no ASC;
+              DENSE_RANK()->best_month_rank,
+              DENSE_RANK(PARTITION BY user_id ORDER BY month_income ASC, month_no ASC)->worst_month_rank}(MI)
+
+AI <- gamma_{user_id;
+             SUM(month_income)->total_income,
+             AVG(month_income)->avg_monthly_income,
+             STDDEV_SAMP(month_income)->income_stddev,
+             MAX(month_income)->best_month_income,
+             MIN(month_income)->worst_month_income,
+             COUNT_IF(month_income>0)->active_income_months}(MI)
+
+BWM <- gamma_{user_id;
+              MAX_IF(month_no, best_month_rank=1)->best_month_no,
+              MAX_IF(month_no, worst_month_rank=1)->worst_month_no}(MIR)
+
+R0 <- FB bowtie_{FB.user_id=AI.user_id} AI bowtie_{FB.user_id=BWM.user_id} BWM
+R1 <- alpha_{(spending_budget+saving_budget+investing_budget+donation_budget)*12->planned_annual_budget,
+             total_income->actual_annual_income,
+             income_stddev/NULLIF(avg_monthly_income,0)->income_volatility_cv,
+             total_income-(spending_budget*12)->annual_free_cash_after_spending,
+             (spending_budget*12)/NULLIF(total_income,0)->spending_pressure_ratio,
+             credit/NULLIF(total_income,0)->leverage_ratio}(R0)
+R  <- omega_{ORDER BY annual_free_cash_after_spending DESC,
+                    spending_pressure_ratio ASC,
+                    user_id ASC;
+             DENSE_RANK()->finance_resilience_rank}(R1)
+
Index: db-scripts/p6-relational-algebra/p6_q2_annual_training_consistency_workload_ra.txt
===================================================================
--- db-scripts/p6-relational-algebra/p6_q2_annual_training_consistency_workload_ra.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ db-scripts/p6-relational-algebra/p6_q2_annual_training_consistency_workload_ra.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,52 @@
+TB <- pi_{tu.user_id, u.username, u.email, tu.gender, tu.age, tu.weight}
+      (training_users tu bowtie_{tu.user_id = u.user_id} users u)
+
+TBM <- TB x M
+TSY <- sigma_{YEAR(ts.date)=Y}(training_sessions ts)
+MS0 <- TBM leftouterjoin_{TBM.user_id = ts.training_user_id AND TBM.month_no = MONTH(ts.date)} TSY
+MS <- gamma_{user_id, month_no;
+             COUNT(ts.training_id)->sessions_count,
+             SUM(COALESCE(ts.duration,0))->total_duration_minutes,
+             SUM(COALESCE(ts.calories,0))->total_calories,
+             AVG(COALESCE(ts.duration,0))->avg_session_duration,
+             AVG(COALESCE(ts.calories,0))->avg_session_calories}(MS0)
+
+MR <- omega_{PARTITION BY user_id ORDER BY total_calories DESC, month_no ASC;
+             DENSE_RANK()->peak_calorie_month_rank,
+             DENSE_RANK(PARTITION BY user_id ORDER BY sessions_count DESC, month_no ASC)->peak_sessions_month_rank}(MS)
+
+AMS <- sigma_{sessions_count>0}(MS)
+AMS1 <- omega_{PARTITION BY user_id ORDER BY month_no;
+               ROW_NUMBER()->rn}(AMS)
+AMS2 <- alpha_{month_no - rn -> grp}(AMS1)
+LS0 <- gamma_{user_id, grp; COUNT(*)->streak_len}(AMS2)
+LS  <- gamma_{user_id; MAX(streak_len)->longest_active_month_streak}(LS0)
+
+AT <- gamma_{user_id;
+             SUM(sessions_count)->annual_sessions,
+             SUM(total_duration_minutes)->annual_duration_minutes,
+             SUM(total_calories)->annual_calories,
+             AVG(total_duration_minutes)->avg_monthly_duration,
+             AVG(total_calories)->avg_monthly_calories,
+             COUNT_IF(sessions_count>0)->active_months,
+             REGR_SLOPE(total_calories, month_no)->calories_trend_slope,
+             REGR_SLOPE(total_duration_minutes, month_no)->duration_trend_slope}(MS)
+
+PM <- gamma_{user_id;
+             MAX_IF(month_no, peak_calorie_month_rank=1)->peak_calorie_month_no,
+             MAX_IF(month_no, peak_sessions_month_rank=1)->peak_sessions_month_no}(MR)
+
+R0 <- TB bowtie_{TB.user_id=AT.user_id} AT
+         bowtie_{TB.user_id=PM.user_id} PM
+         leftouterjoin_{TB.user_id=LS.user_id} LS
+R1 <- alpha_{active_months/12.0->consistency_ratio,
+             COALESCE(longest_active_month_streak,0)->longest_active_month_streak_nz,
+             COALESCE(calories_trend_slope,0)->calories_trend_slope_nz,
+             COALESCE(duration_trend_slope,0)->duration_trend_slope_nz}(R0)
+R  <- omega_{ORDER BY annual_calories DESC,
+                    active_months DESC,
+                    longest_active_month_streak_nz DESC,
+                    user_id ASC;
+             DENSE_RANK()->training_annual_rank}(R1)
+
+
Index: db-scripts/p6-relational-algebra/p6_q3_annual_discipline_adherence_streaks_ra.txt
===================================================================
--- db-scripts/p6-relational-algebra/p6_q3_annual_discipline_adherence_streaks_ra.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ db-scripts/p6-relational-algebra/p6_q3_annual_discipline_adherence_streaks_ra.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,56 @@
+DB <- pi_{du.user_id, u.username, u.email}
+      (discipline_users du bowtie_{du.user_id = u.user_id} users u)
+
+TC <- tasks t leftouterjoin_{t.custom_tracking_id = c.custom_tracking_id} custom_tracking_categories c
+TM0 <- alpha_{COALESCE(t.discipline_user_id, c.user_id)->owner_user_id}(TC)
+TM1 <- sigma_{t.discipline_user_id IS NOT NULL OR t.custom_tracking_id IS NOT NULL}(TM0)
+TM <- gamma_{owner_user_id;
+             COUNT(*)->total_tasks_defined,
+             COUNT_IF(t.custom_tracking_id IS NULL)->core_tasks,
+             COUNT_IF(t.custom_tracking_id IS NOT NULL)->custom_tasks,
+             COUNT_DISTINCT(COALESCE(t.custom_tracking_id,'core'))->task_category_span}(TM1)
+
+ADC0 <- sigma_{YEAR(dc.date)=Y}(daily_completion dc)
+ADC <- alpha_{COALESCE(dc.procent,0)->procent,
+              CASE(procent>=80,1,0)->strong_day}(ADC0)
+
+DCS <- gamma_{user_id;
+              COUNT(*)->tracked_days,
+              AVG(procent)->avg_completion_percent,
+              PERCENTILE_CONT_0_5(procent)->median_completion_percent,
+              COUNT_IF(procent=100)->perfect_days,
+              COUNT_IF(procent>=80)->strong_days,
+              STDDEV_SAMP(procent)->completion_variability}(ADC)
+
+SDS0 <- sigma_{strong_day=1}(ADC)
+SDS1 <- omega_{PARTITION BY user_id ORDER BY date; ROW_NUMBER()->rn}(SDS0)
+SDS2 <- alpha_{date - rn -> grp}(SDS1)
+LSS0 <- gamma_{user_id, grp; COUNT(*)->streak_len}(SDS2)
+LSS  <- gamma_{user_id; MAX(streak_len)->longest_strong_day_streak}(LSS0)
+
+ATE0 <- ADC0 leftouterjoin_{ADC0.daily_completion_id = tdc.daily_completion_id} task_daily_completion tdc
+ATE  <- gamma_{ADC0.user_id; COUNT(tdc.task_id)->completed_task_events}(ATE0)
+
+R0 <- DB
+      leftouterjoin_{DB.user_id = TM.owner_user_id} TM
+      leftouterjoin_{DB.user_id = DCS.user_id} DCS
+      leftouterjoin_{DB.user_id = ATE.user_id} ATE
+      leftouterjoin_{DB.user_id = LSS.user_id} LSS
+R1 <- alpha_{COALESCE(total_tasks_defined,0)->total_tasks_defined_nz,
+             COALESCE(core_tasks,0)->core_tasks_nz,
+             COALESCE(custom_tasks,0)->custom_tasks_nz,
+             COALESCE(task_category_span,0)->task_category_span_nz,
+             COALESCE(tracked_days,0)->tracked_days_nz,
+             COALESCE(avg_completion_percent,0)->avg_completion_percent_nz,
+             COALESCE(median_completion_percent,0)->median_completion_percent_nz,
+             COALESCE(perfect_days,0)->perfect_days_nz,
+             COALESCE(strong_days,0)->strong_days_nz,
+             COALESCE(completion_variability,0)->completion_variability_nz,
+             COALESCE(completed_task_events,0)->completed_task_events_nz,
+             COALESCE(longest_strong_day_streak,0)->longest_strong_day_streak_nz,
+             COALESCE(strong_days/NULLIF(tracked_days,0),0)->strong_day_ratio,
+             (COALESCE(avg_completion_percent,0)*0.45 +
+              COALESCE(longest_strong_day_streak,0)*2.00 +
+              COALESCE(completed_task_events,0)*0.35)->discipline_composite_score}(R0)
+R  <- omega_{ORDER BY discipline_composite_score DESC, user_id ASC;
+             DENSE_RANK()->discipline_annual_rank}(R1)
Index: db-scripts/p6-relational-algebra/p6_q4_annual_investing_diversification_concentration_ra.txt
===================================================================
--- db-scripts/p6-relational-algebra/p6_q4_annual_investing_diversification_concentration_ra.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ db-scripts/p6-relational-algebra/p6_q4_annual_investing_diversification_concentration_ra.txt	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,64 @@
+
+IB <- pi_{iu.user_id, u.username, u.email}
+      (investor_users iu bowtie_{iu.user_id = u.user_id} users u)
+
+AAL <- pi_{a.user_id, a.ticker_symbol,
+           COALESCE(a.quantity,0)->quantity,
+           COALESCE(a.buy_price,0)->buy_price,
+           COALESCE(a.quantity,0)*COALESCE(a.buy_price,0)->invested_amount,
+           a.buy_date}
+       (sigma_{YEAR(a.buy_date)=Y}(assets a))
+
+TR <- gamma_{user_id, ticker_symbol;
+             SUM(quantity)->total_quantity,
+             SUM(invested_amount)->total_invested_amount,
+             COUNT(*)->lot_count,
+             MIN(buy_date)->first_buy_date,
+             MAX(buy_date)->last_buy_date}(AAL)
+
+PT <- gamma_{user_id;
+             SUM(total_invested_amount)->annual_total_invested,
+             SUM(lot_count)->annual_lot_count,
+             COUNT(*)->distinct_tickers}(TR)
+
+W0 <- TR bowtie_{TR.user_id = PT.user_id} PT
+W1 <- alpha_{total_invested_amount/NULLIF(annual_total_invested,0)->position_weight}(W0)
+W  <- omega_{PARTITION BY user_id ORDER BY total_invested_amount DESC, ticker_symbol ASC;
+             DENSE_RANK()->position_rank}(W1)
+
+C <- gamma_{user_id;
+            SUM(position_weight*position_weight)->hhi_concentration,
+            MAX(position_weight)->top_position_weight,
+            MAX_IF(ticker_symbol, position_rank=1)->top_ticker}(W)
+
+IBM <- IB x M
+AY  <- sigma_{YEAR(a.buy_date)=Y}(assets a)
+MI0 <- IBM leftouterjoin_{IBM.user_id=a.user_id AND IBM.month_no=MONTH(a.buy_date)} AY
+MI  <- gamma_{user_id, month_no;
+              SUM(COALESCE(a.quantity,0)*COALESCE(a.buy_price,0))->monthly_invested_amount}(MI0)
+MS  <- gamma_{user_id;
+              AVG(monthly_invested_amount)->avg_monthly_contribution,
+              STDDEV_SAMP(monthly_invested_amount)->contribution_stddev,
+              COUNT_IF(monthly_invested_amount>0)->active_investing_months}(MI)
+
+R0 <- IB
+      leftouterjoin_{IB.user_id=PT.user_id} PT
+      leftouterjoin_{IB.user_id=C.user_id} C
+      leftouterjoin_{IB.user_id=MS.user_id} MS
+R1 <- alpha_{COALESCE(annual_total_invested,0)->annual_total_invested_nz,
+             COALESCE(annual_lot_count,0)->annual_lot_count_nz,
+             COALESCE(distinct_tickers,0)->distinct_tickers_nz,
+             COALESCE(avg_monthly_contribution,0)->avg_monthly_contribution_nz,
+             COALESCE(active_investing_months,0)->active_investing_months_nz,
+             COALESCE(active_investing_months,0)/12.0->activity_ratio,
+             COALESCE(hhi_concentration,0)->hhi_concentration_nz,
+             1-COALESCE(hhi_concentration,1)->diversification_index,
+             COALESCE(top_position_weight,0)->top_position_weight_nz,
+             COALESCE(contribution_stddev/NULLIF(avg_monthly_contribution,0),0)->contribution_volatility_cv}(R0)
+R  <- omega_{ORDER BY diversification_index DESC,
+                    annual_total_invested_nz DESC,
+                    active_investing_months_nz DESC,
+                    user_id ASC;
+             DENSE_RANK()->investing_annual_rank}(R1)
+
+
Index: db-scripts/p6_q1_annual_finance_budget_resilience.sql
===================================================================
--- db-scripts/p6_q1_annual_finance_budget_resilience.sql	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ db-scripts/p6_q1_annual_finance_budget_resilience.sql	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,102 @@
+SET search_path TO trekr;
+
+-- P6 Q1 (Annual): Finance budget resilience and income stability leaderboard.
+-- Change report_year in params CTE when needed.
+WITH params AS (
+    SELECT 2026::int AS report_year
+),
+months AS (
+    SELECT generate_series(1, 12) AS month_no
+),
+finance_base AS (
+    SELECT
+        fu.user_id,
+        u.username,
+        u.email,
+        COALESCE(fu.spending_budget, 0) AS spending_budget,
+        COALESCE(fu.saving_budget, 0) AS saving_budget,
+        COALESCE(fu.investing_budget, 0) AS investing_budget,
+        COALESCE(fu.donation_budget, 0) AS donation_budget,
+        COALESCE(fu.credit, 0) AS credit
+    FROM finance_users fu
+    JOIN users u ON u.user_id = fu.user_id
+),
+monthly_income AS (
+    SELECT
+        fb.user_id,
+        m.month_no,
+        COALESCE(SUM(i.amount), 0) AS month_income
+    FROM finance_base fb
+    CROSS JOIN months m
+    LEFT JOIN incomes i
+        ON i.user_id = fb.user_id
+       AND EXTRACT(YEAR FROM i.date)::int = (SELECT report_year FROM params)
+       AND EXTRACT(MONTH FROM i.date)::int = m.month_no
+    GROUP BY fb.user_id, m.month_no
+),
+monthly_income_ranked AS (
+    SELECT
+        mi.*,
+        DENSE_RANK() OVER (PARTITION BY mi.user_id ORDER BY mi.month_income DESC, mi.month_no ASC) AS best_month_rank,
+        DENSE_RANK() OVER (PARTITION BY mi.user_id ORDER BY mi.month_income ASC, mi.month_no ASC) AS worst_month_rank
+    FROM monthly_income mi
+),
+annual_income AS (
+    SELECT
+        user_id,
+        SUM(month_income) AS total_income,
+        AVG(month_income) AS avg_monthly_income,
+        STDDEV_SAMP(month_income) AS income_stddev,
+        MAX(month_income) AS best_month_income,
+        MIN(month_income) AS worst_month_income,
+        COUNT(*) FILTER (WHERE month_income > 0) AS active_income_months
+    FROM monthly_income
+    GROUP BY user_id
+),
+best_worst_months AS (
+    SELECT
+        user_id,
+        MAX(month_no) FILTER (WHERE best_month_rank = 1) AS best_month_no,
+        MAX(month_no) FILTER (WHERE worst_month_rank = 1) AS worst_month_no
+    FROM monthly_income_ranked
+    GROUP BY user_id
+)
+SELECT
+    fb.user_id,
+    fb.username,
+    fb.email,
+    (fb.spending_budget + fb.saving_budget + fb.investing_budget + fb.donation_budget) * 12 AS planned_annual_budget,
+    ai.total_income AS actual_annual_income,
+    ai.avg_monthly_income,
+    ai.active_income_months,
+    ai.best_month_income,
+    ai.worst_month_income,
+    bwm.best_month_no,
+    bwm.worst_month_no,
+    ROUND(
+        (ai.income_stddev / NULLIF(ai.avg_monthly_income, 0))::numeric,
+        4
+    ) AS income_volatility_cv,
+    ROUND(
+        (ai.total_income - (fb.spending_budget * 12))::numeric,
+        2
+    ) AS annual_free_cash_after_spending,
+    ROUND(
+        ((fb.spending_budget * 12) / NULLIF(ai.total_income, 0))::numeric,
+        4
+    ) AS spending_pressure_ratio,
+    ROUND(
+        (fb.credit / NULLIF(ai.total_income, 0))::numeric,
+        4
+    ) AS leverage_ratio,
+    DENSE_RANK() OVER (
+        ORDER BY
+            (ai.total_income - (fb.spending_budget * 12)) DESC,
+            ((fb.spending_budget * 12) / NULLIF(ai.total_income, 0)) ASC,
+            fb.user_id ASC
+    ) AS finance_resilience_rank
+FROM finance_base fb
+JOIN annual_income ai ON ai.user_id = fb.user_id
+JOIN best_worst_months bwm ON bwm.user_id = fb.user_id
+ORDER BY finance_resilience_rank, fb.user_id;
+
Index: db-scripts/p6_q2_annual_training_consistency_workload.sql
===================================================================
--- db-scripts/p6_q2_annual_training_consistency_workload.sql	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ db-scripts/p6_q2_annual_training_consistency_workload.sql	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,116 @@
+SET search_path TO trekr;
+
+-- P6 Q2 (Annual): Training consistency, workload trend, and peak period analysis.
+-- Change report_year in params CTE when needed.
+WITH params AS (
+    SELECT 2026::int AS report_year
+),
+months AS (
+    SELECT generate_series(1, 12) AS month_no
+),
+training_base AS (
+    SELECT
+        tu.user_id,
+        u.username,
+        u.email,
+        tu.gender,
+        tu.age,
+        tu.weight
+    FROM training_users tu
+    JOIN users u ON u.user_id = tu.user_id
+),
+monthly_sessions AS (
+    SELECT
+        tb.user_id,
+        m.month_no,
+        COALESCE(COUNT(ts.training_id), 0) AS sessions_count,
+        COALESCE(SUM(ts.duration), 0) AS total_duration_minutes,
+        COALESCE(SUM(ts.calories), 0) AS total_calories,
+        COALESCE(AVG(ts.duration), 0) AS avg_session_duration,
+        COALESCE(AVG(ts.calories), 0) AS avg_session_calories
+    FROM training_base tb
+    CROSS JOIN months m
+    LEFT JOIN training_sessions ts
+        ON ts.training_user_id = tb.user_id
+       AND EXTRACT(YEAR FROM ts.date)::int = (SELECT report_year FROM params)
+       AND EXTRACT(MONTH FROM ts.date)::int = m.month_no
+    GROUP BY tb.user_id, m.month_no
+),
+monthly_ranked AS (
+    SELECT
+        ms.*,
+        DENSE_RANK() OVER (PARTITION BY ms.user_id ORDER BY ms.total_calories DESC, ms.month_no ASC) AS peak_calorie_month_rank,
+        DENSE_RANK() OVER (PARTITION BY ms.user_id ORDER BY ms.sessions_count DESC, ms.month_no ASC) AS peak_sessions_month_rank
+    FROM monthly_sessions ms
+),
+active_month_streaks AS (
+    SELECT
+        user_id,
+        month_no,
+        month_no - ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY month_no) AS grp
+    FROM monthly_sessions
+    WHERE sessions_count > 0
+),
+longest_streak AS (
+    SELECT
+        user_id,
+        MAX(streak_len) AS longest_active_month_streak
+    FROM (
+        SELECT user_id, grp, COUNT(*) AS streak_len
+        FROM active_month_streaks
+        GROUP BY user_id, grp
+    ) s
+    GROUP BY user_id
+),
+annual_training AS (
+    SELECT
+        user_id,
+        SUM(sessions_count) AS annual_sessions,
+        SUM(total_duration_minutes) AS annual_duration_minutes,
+        SUM(total_calories) AS annual_calories,
+        AVG(total_duration_minutes) AS avg_monthly_duration,
+        AVG(total_calories) AS avg_monthly_calories,
+        COUNT(*) FILTER (WHERE sessions_count > 0) AS active_months,
+        REGR_SLOPE(total_calories::numeric, month_no::numeric) AS calories_trend_slope,
+        REGR_SLOPE(total_duration_minutes::numeric, month_no::numeric) AS duration_trend_slope
+    FROM monthly_sessions
+    GROUP BY user_id
+),
+peak_months AS (
+    SELECT
+        user_id,
+        MAX(month_no) FILTER (WHERE peak_calorie_month_rank = 1) AS peak_calorie_month_no,
+        MAX(month_no) FILTER (WHERE peak_sessions_month_rank = 1) AS peak_sessions_month_no
+    FROM monthly_ranked
+    GROUP BY user_id
+)
+SELECT
+    tb.user_id,
+    tb.username,
+    tb.email,
+    tb.gender,
+    tb.age,
+    tb.weight,
+    at.annual_sessions,
+    ROUND(at.annual_duration_minutes::numeric, 2) AS annual_duration_minutes,
+    ROUND(at.annual_calories::numeric, 2) AS annual_calories,
+    at.active_months,
+    ROUND((at.active_months / 12.0)::numeric, 4) AS consistency_ratio,
+    COALESCE(ls.longest_active_month_streak, 0) AS longest_active_month_streak,
+    pm.peak_calorie_month_no,
+    pm.peak_sessions_month_no,
+    ROUND(COALESCE(at.calories_trend_slope, 0)::numeric, 4) AS calories_trend_slope,
+    ROUND(COALESCE(at.duration_trend_slope, 0)::numeric, 4) AS duration_trend_slope,
+    DENSE_RANK() OVER (
+        ORDER BY
+            at.annual_calories DESC,
+            at.active_months DESC,
+            COALESCE(ls.longest_active_month_streak, 0) DESC,
+            tb.user_id ASC
+    ) AS training_annual_rank
+FROM training_base tb
+JOIN annual_training at ON at.user_id = tb.user_id
+JOIN peak_months pm ON pm.user_id = tb.user_id
+LEFT JOIN longest_streak ls ON ls.user_id = tb.user_id
+ORDER BY training_annual_rank, tb.user_id;
+
Index: db-scripts/p6_q3_annual_discipline_adherence_streaks.sql
===================================================================
--- db-scripts/p6_q3_annual_discipline_adherence_streaks.sql	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ db-scripts/p6_q3_annual_discipline_adherence_streaks.sql	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,124 @@
+SET search_path TO trekr;
+
+-- P6 Q3 (Annual): Discipline adherence, completion quality, and streak behavior.
+-- Change report_year in params CTE when needed.
+WITH params AS (
+    SELECT 2026::int AS report_year
+),
+discipline_base AS (
+    SELECT
+        du.user_id,
+        u.username,
+        u.email
+    FROM discipline_users du
+    JOIN users u ON u.user_id = du.user_id
+),
+task_mix AS (
+    SELECT
+        COALESCE(t.discipline_user_id, c.user_id) AS user_id,
+        COUNT(*) AS total_tasks_defined,
+        COUNT(*) FILTER (WHERE t.custom_tracking_id IS NULL) AS core_tasks,
+        COUNT(*) FILTER (WHERE t.custom_tracking_id IS NOT NULL) AS custom_tasks,
+        COUNT(DISTINCT COALESCE(t.custom_tracking_id::text, 'core')) AS task_category_span
+    FROM tasks t
+    LEFT JOIN custom_tracking_categories c
+        ON c.custom_tracking_id = t.custom_tracking_id
+    WHERE t.discipline_user_id IS NOT NULL
+       OR t.custom_tracking_id IS NOT NULL
+    GROUP BY COALESCE(t.discipline_user_id, c.user_id)
+),
+annual_daily_completion AS (
+    SELECT
+        dc.user_id,
+        dc.date,
+        COALESCE(dc.procent, 0) AS procent,
+        CASE WHEN COALESCE(dc.procent, 0) >= 80 THEN 1 ELSE 0 END AS strong_day
+    FROM daily_completion dc
+    WHERE EXTRACT(YEAR FROM dc.date)::int = (SELECT report_year FROM params)
+),
+daily_completion_stats AS (
+    SELECT
+        adc.user_id,
+        COUNT(*) AS tracked_days,
+        AVG(adc.procent) AS avg_completion_percent,
+        PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY adc.procent) AS median_completion_percent,
+        COUNT(*) FILTER (WHERE adc.procent = 100) AS perfect_days,
+        COUNT(*) FILTER (WHERE adc.procent >= 80) AS strong_days,
+        STDDEV_SAMP(adc.procent) AS completion_variability
+    FROM annual_daily_completion adc
+    GROUP BY adc.user_id
+),
+strong_day_streaks AS (
+    SELECT
+        user_id,
+        date,
+        date - (ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY date))::int AS grp
+    FROM annual_daily_completion
+    WHERE strong_day = 1
+),
+longest_strong_streak AS (
+    SELECT
+        user_id,
+        MAX(streak_len) AS longest_strong_day_streak
+    FROM (
+        SELECT user_id, grp, COUNT(*) AS streak_len
+        FROM strong_day_streaks
+        GROUP BY user_id, grp
+    ) s
+    GROUP BY user_id
+),
+annual_task_execution AS (
+    SELECT
+        dc.user_id,
+        COUNT(tdc.task_id) AS completed_task_events
+    FROM daily_completion dc
+    LEFT JOIN task_daily_completion tdc
+        ON tdc.daily_completion_id = dc.daily_completion_id
+    WHERE EXTRACT(YEAR FROM dc.date)::int = (SELECT report_year FROM params)
+    GROUP BY dc.user_id
+)
+SELECT
+    db.user_id,
+    db.username,
+    db.email,
+    COALESCE(tm.total_tasks_defined, 0) AS total_tasks_defined,
+    COALESCE(tm.core_tasks, 0) AS core_tasks,
+    COALESCE(tm.custom_tasks, 0) AS custom_tasks,
+    COALESCE(tm.task_category_span, 0) AS task_category_span,
+    COALESCE(dcs.tracked_days, 0) AS tracked_days,
+    ROUND(COALESCE(dcs.avg_completion_percent, 0)::numeric, 2) AS avg_completion_percent,
+    ROUND(COALESCE(dcs.median_completion_percent, 0)::numeric, 2) AS median_completion_percent,
+    COALESCE(dcs.perfect_days, 0) AS perfect_days,
+    COALESCE(dcs.strong_days, 0) AS strong_days,
+    ROUND(COALESCE(dcs.completion_variability, 0)::numeric, 4) AS completion_variability,
+    COALESCE(ate.completed_task_events, 0) AS completed_task_events,
+    COALESCE(lss.longest_strong_day_streak, 0) AS longest_strong_day_streak,
+    ROUND(
+        COALESCE((COALESCE(dcs.strong_days, 0) / NULLIF(COALESCE(dcs.tracked_days, 0), 0)::numeric), 0),
+        4
+    ) AS strong_day_ratio,
+    ROUND(
+        (
+            COALESCE(dcs.avg_completion_percent, 0) * 0.45
+            + COALESCE(lss.longest_strong_day_streak, 0) * 2.00
+            + COALESCE(ate.completed_task_events, 0) * 0.35
+        )::numeric,
+        2
+    ) AS discipline_composite_score,
+    DENSE_RANK() OVER (
+        ORDER BY
+            (
+                COALESCE(dcs.avg_completion_percent, 0) * 0.45
+                + COALESCE(lss.longest_strong_day_streak, 0) * 2.00
+                + COALESCE(ate.completed_task_events, 0) * 0.35
+            ) DESC,
+            db.user_id ASC
+    ) AS discipline_annual_rank
+FROM discipline_base db
+LEFT JOIN task_mix tm ON tm.user_id = db.user_id
+LEFT JOIN daily_completion_stats dcs ON dcs.user_id = db.user_id
+LEFT JOIN annual_task_execution ate ON ate.user_id = db.user_id
+LEFT JOIN longest_strong_streak lss ON lss.user_id = db.user_id
+ORDER BY discipline_annual_rank, db.user_id;
+
+
Index: db-scripts/p6_q4_annual_investing_diversification_concentration.sql
===================================================================
--- db-scripts/p6_q4_annual_investing_diversification_concentration.sql	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
+++ db-scripts/p6_q4_annual_investing_diversification_concentration.sql	(revision 89156c1feb4b06a2d4ecb2bbac89f7c39f0df7e2)
@@ -0,0 +1,123 @@
+SET search_path TO trekr;
+
+-- P6 Q4 (Annual): Investing diversification, concentration risk, and contribution pacing.
+-- Change report_year in params CTE when needed.
+WITH params AS (
+    SELECT 2026::int AS report_year
+),
+months AS (
+    SELECT generate_series(1, 12) AS month_no
+),
+investor_base AS (
+    SELECT
+        iu.user_id,
+        u.username,
+        u.email
+    FROM investor_users iu
+    JOIN users u ON u.user_id = iu.user_id
+),
+annual_asset_lots AS (
+    SELECT
+        a.user_id,
+        a.ticker_symbol,
+        COALESCE(a.quantity, 0) AS quantity,
+        COALESCE(a.buy_price, 0) AS buy_price,
+        COALESCE(a.quantity, 0) * COALESCE(a.buy_price, 0) AS invested_amount,
+        a.buy_date
+    FROM assets a
+    WHERE EXTRACT(YEAR FROM a.buy_date)::int = (SELECT report_year FROM params)
+),
+ticker_rollup AS (
+    SELECT
+        aal.user_id,
+        aal.ticker_symbol,
+        SUM(aal.quantity) AS total_quantity,
+        SUM(aal.invested_amount) AS total_invested_amount,
+        COUNT(*) AS lot_count,
+        MIN(aal.buy_date) AS first_buy_date,
+        MAX(aal.buy_date) AS last_buy_date
+    FROM annual_asset_lots aal
+    GROUP BY aal.user_id, aal.ticker_symbol
+),
+portfolio_totals AS (
+    SELECT
+        user_id,
+        SUM(total_invested_amount) AS annual_total_invested,
+        SUM(lot_count) AS annual_lot_count,
+        COUNT(*) AS distinct_tickers
+    FROM ticker_rollup
+    GROUP BY user_id
+),
+weights AS (
+    SELECT
+        tr.user_id,
+        tr.ticker_symbol,
+        tr.total_invested_amount,
+        pt.annual_total_invested,
+        (tr.total_invested_amount / NULLIF(pt.annual_total_invested, 0)) AS position_weight,
+        DENSE_RANK() OVER (
+            PARTITION BY tr.user_id
+            ORDER BY tr.total_invested_amount DESC, tr.ticker_symbol ASC
+        ) AS position_rank
+    FROM ticker_rollup tr
+    JOIN portfolio_totals pt ON pt.user_id = tr.user_id
+),
+concentration AS (
+    SELECT
+        user_id,
+        SUM(position_weight * position_weight) AS hhi_concentration,
+        MAX(position_weight) AS top_position_weight,
+        MAX(ticker_symbol) FILTER (WHERE position_rank = 1) AS top_ticker
+    FROM weights
+    GROUP BY user_id
+),
+monthly_investment AS (
+    SELECT
+        ib.user_id,
+        m.month_no,
+        COALESCE(SUM(a.quantity * a.buy_price), 0) AS monthly_invested_amount
+    FROM investor_base ib
+    CROSS JOIN months m
+    LEFT JOIN assets a
+        ON a.user_id = ib.user_id
+       AND EXTRACT(YEAR FROM a.buy_date)::int = (SELECT report_year FROM params)
+       AND EXTRACT(MONTH FROM a.buy_date)::int = m.month_no
+    GROUP BY ib.user_id, m.month_no
+),
+monthly_investment_stats AS (
+    SELECT
+        user_id,
+        AVG(monthly_invested_amount) AS avg_monthly_contribution,
+        STDDEV_SAMP(monthly_invested_amount) AS contribution_stddev,
+        COUNT(*) FILTER (WHERE monthly_invested_amount > 0) AS active_investing_months
+    FROM monthly_investment
+    GROUP BY user_id
+)
+SELECT
+    ib.user_id,
+    ib.username,
+    ib.email,
+    COALESCE(pt.annual_total_invested, 0) AS annual_total_invested,
+    COALESCE(pt.annual_lot_count, 0) AS annual_lot_count,
+    COALESCE(pt.distinct_tickers, 0) AS distinct_tickers,
+    ROUND(COALESCE(ms.avg_monthly_contribution, 0)::numeric, 2) AS avg_monthly_contribution,
+    COALESCE(ms.active_investing_months, 0) AS active_investing_months,
+    ROUND((COALESCE(ms.active_investing_months, 0) / 12.0)::numeric, 4) AS activity_ratio,
+    ROUND(COALESCE(c.hhi_concentration, 0)::numeric, 4) AS hhi_concentration,
+    ROUND((1 - COALESCE(c.hhi_concentration, 1))::numeric, 4) AS diversification_index,
+    ROUND(COALESCE(c.top_position_weight, 0)::numeric, 4) AS top_position_weight,
+    c.top_ticker,
+    ROUND((COALESCE(ms.contribution_stddev, 0) / NULLIF(ms.avg_monthly_contribution, 0))::numeric, 4) AS contribution_volatility_cv,
+    DENSE_RANK() OVER (
+        ORDER BY
+            (1 - COALESCE(c.hhi_concentration, 1)) DESC,
+            COALESCE(pt.annual_total_invested, 0) DESC,
+            COALESCE(ms.active_investing_months, 0) DESC,
+            ib.user_id ASC
+    ) AS investing_annual_rank
+FROM investor_base ib
+LEFT JOIN portfolio_totals pt ON pt.user_id = ib.user_id
+LEFT JOIN concentration c ON c.user_id = ib.user_id
+LEFT JOIN monthly_investment_stats ms ON ms.user_id = ib.user_id
+ORDER BY investing_annual_rank, ib.user_id;
+