| Version 1 (modified by , 2 weeks ago) ( diff ) |
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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)
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