Changes between Initial Version and Version 1 of AdvancedReportsAIusage

Timestamp:

09/24/26 13:42:52 (2 days ago)

Author:

231285

Comment:

--

AdvancedReportsAIusage

@@ +1 @@
+= Advanced Reports AI Usage =
+
+== Name of AI service/solution that was used ==
+
+'''Claude Code''' (Anthropic)
+
+ * '''URL:''' `https://claude.com/claude-code`
+ * '''Type of service/subscription:''' Claude subscription, model Claude Sonnet 5.
+
+== Final result ==
+
+=== Diagram ===
+
+None. Both reports read `transactions`, `market_trades`, `orders`, `markets`, `crypto` and
+`users` exactly as they already existed after
+Normalization — no attribute or relation was missing,
+so ERModel and
+!RelationalDesign needed no changes and there is
+no new diagram for this phase. This is stated explicitly rather than left implicit because the
+phase rubric specifically calls out modifying the design as the fallback when a good report
+idea can't be answered by the data on hand — it wasn't needed here.
+
+=== Results in details / description ===
+
+The AI:
+
+ * Turned my two fully-specified report questions (the exact P/L, ROI, consistency, volume,
+   return, volatility and participation formulas were mine) into two single-statement SQL
+   queries, each wrapped as a `LANGUAGE sql STABLE` function
+   (`project.report_top_traders`, `project.report_market_performance`) in
+   `schema_creation.sql`, so the phase's "just one SQL
+   query" requirement is met by the query text itself, while still giving the prototype a
+   clean, parameterised, named thing to call.
+
+The two functions, from `schema_creation.sql`:
+
+{{{
+-- report_top_traders: realized trading performance per user over [p_from, p_to),
+-- bucketed into quarters to measure how consistently each user was profitable.
+CREATE OR REPLACE FUNCTION project.report_top_traders(p_from timestamptz, p_to timestamptz)
+RETURNS TABLE (
+    username            varchar,
+    realized_pl         numeric,
+    total_invested      numeric,
+    roi_pct             numeric,
+    profitable_periods  bigint,
+    losing_periods      bigint,
+    total_periods       bigint,
+    consistency_pct     numeric
+)
+LANGUAGE sql STABLE AS $$
+    WITH period_pl AS (
+        SELECT
+            t.user_id,
+            date_trunc('quarter', t.created_at)          AS period,
+            SUM(t.amount)                                AS period_pl,
+            SUM(t.amount) FILTER (WHERE t.type = 'buy')  AS period_buy
+        FROM project.transactions t
+        WHERE t.type IN ('buy', 'sell', 'fee')
+          AND t.created_at >= p_from
+          AND t.created_at <  p_to
+        GROUP BY t.user_id, date_trunc('quarter', t.created_at)
+    )
+    SELECT
+        u.username,
+        SUM(pp.period_pl)                                                       AS realized_pl,
+        ABS(SUM(pp.period_buy))                                                 AS total_invested,
+        ROUND(SUM(pp.period_pl) / NULLIF(ABS(SUM(pp.period_buy)), 0) * 100, 2)  AS roi_pct,
+        COUNT(*) FILTER (WHERE pp.period_pl > 0)                                AS profitable_periods,
+        COUNT(*) FILTER (WHERE pp.period_pl < 0)                                AS losing_periods,
+        COUNT(*)                                                                AS total_periods,
+        ROUND(COUNT(*) FILTER (WHERE pp.period_pl > 0)::numeric
+              / NULLIF(COUNT(*), 0) * 100, 2)                                   AS consistency_pct
+    FROM period_pl pp
+    JOIN project.users u ON u.id = pp.user_id
+    GROUP BY u.id, u.username
+    ORDER BY realized_pl DESC;
+$$;
+
+-- report_market_performance: trading activity and price behaviour per market
+-- over [p_from, p_to). Volume/trade-count/price stats come from market_trades
+-- (the complete tape — user fills and simulated fills alike); participating
+-- users can only come from orders, since market_trades has no user_id column.
+CREATE OR REPLACE FUNCTION project.report_market_performance(p_from timestamptz, p_to timestamptz)
+RETURNS TABLE (
+    symbol               varchar,
+    quote_currency       char(3),
+    total_volume         numeric,
+    trade_count          bigint,
+    avg_price            numeric,
+    market_return_pct    numeric,
+    participating_users  bigint
+)
+LANGUAGE sql STABLE AS $$
+    WITH trades AS (
+        SELECT
+            market_id, price, quantity, executed_at,
+            FIRST_VALUE(price) OVER w AS first_price,
+            LAST_VALUE(price)  OVER (PARTITION BY market_id ORDER BY executed_at
+                                      ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS last_price
+        FROM project.market_trades
+        WHERE executed_at >= p_from AND executed_at < p_to
+        WINDOW w AS (PARTITION BY market_id ORDER BY executed_at)
+    ),
+    market_stats AS (
+        SELECT
+            market_id,
+            SUM(quantity)    AS total_volume,
+            COUNT(*)         AS trade_count,
+            AVG(price)       AS avg_price,
+            MAX(first_price) AS first_price,
+            MAX(last_price)  AS last_price
+        FROM trades
+        GROUP BY market_id
+    ),
+    participation AS (
+        SELECT market_id, COUNT(DISTINCT user_id) AS participating_users
+        FROM project.orders
+        WHERE status = 'executed' AND executed_at >= p_from AND executed_at < p_to
+        GROUP BY market_id
+    )
+    SELECT
+        c.symbol,
+        m.quote_currency,
+        ms.total_volume,
+        ms.trade_count,
+        ROUND(ms.avg_price, 6)                                                            AS avg_price,
+        ROUND((ms.last_price - ms.first_price) / NULLIF(ms.first_price, 0) * 100, 2)      AS market_return_pct,
+        COALESCE(p.participating_users, 0)                                                AS participating_users
+    FROM market_stats ms
+    JOIN project.markets m ON m.id = ms.market_id
+    JOIN project.crypto  c ON c.id = m.crypto_id
+    LEFT JOIN participation p ON p.market_id = ms.market_id
+    ORDER BY ms.total_volume DESC;
+$$;
+}}}
+
+ * Wired both into the running CLI as real menu options — `server/reports.go`, options
+   `[10]`/`[11]` in `server/cli.go` — rather than leaving them as documentation-only SQL, per
+   the phase's own framing ("used as reports within your application").
+ * Wrote the relational-algebra equivalent of each query, including how to express
+   `FIRST_VALUE`/`LAST_VALUE` (which have no classical RA equivalent) as an aggregation for the
+   boundary timestamp followed by a self-join, and how to express `FILTER (WHERE …)`-style
+   conditional counts as separate groupings recombined with left outer joins.
+ * Noticed that the existing `data_load.sql` seed data (a few minutes of trade history) cannot
+   demonstrate either report meaningfully — everything falls into one quarter, so "consistency"
+   and "market return over time" have nothing to show — and wrote
+   `reports_demo_data.sql`, an optional, separate,
+   idempotent script adding five quarters of synthetic transactions, market trades and executed
+   orders, deliberately excluded from `-init`/`-load-data` so it cannot disturb the balances the
+   other use cases' documented "verified run" sections depend on.
+
+`reports_demo_data.sql`:
+
+{{{
+-- reports_demo_data.sql
+-- EduBerza - optional historical data for the P6 reports
+-- Course: Databases 2025/2026 Winter, FINKI UKIM
+--
+-- data_load.sql only seeds ~10 minutes of trade history, which is enough to
+-- demonstrate UC0001-UC0007 but not enough to show report_top_traders() or
+-- report_market_performance() doing anything interesting: everything falls
+-- into a single quarter, so "number of profitable periods" and "consistency"
+-- are trivial and "market return" has almost no history to work with.
+--
+-- This script adds five quarters of synthetic transactions, market trades and
+-- executed orders on top of an already-loaded data_load.sql, spanning
+-- 2025-07 to 2026-07, so the two P6 reports have several periods and two
+-- markets with opposite price trends to actually compare.
+--
+-- Deliberately NOT part of -init / -load-data: it only inserts into
+-- transactions, market_trades and orders, and does not touch
+-- users.available_balance/invested_balance or holdings, so it does not
+-- disturb the balances the other use cases' documented "verified run"
+-- sections depend on. Run it by hand, after data_load.sql, only to exercise
+-- the two reports:
+--
+--   psql "$DATABASE_URL" -f server/db/schema_creation.sql
+--   psql "$DATABASE_URL" -f server/db/data_load.sql
+--   psql "$DATABASE_URL" -f server/db/reports_demo_data.sql
+--
+-- Idempotent: deletes its own previously-inserted rows (tagged via
+-- description/source) before re-inserting.
+
+SET search_path TO project, public;
+
+DELETE FROM transactions  WHERE description = 'P6 demo data';
+DELETE FROM orders        WHERE id IN (
+    'e1111111-1111-1111-1111-111111111111', 'e2222222-2222-2222-2222-222222222222',
+    'e3333333-3333-3333-3333-333333333333', 'e4444444-4444-4444-4444-444444444444',
+    'e5555555-5555-5555-5555-555555555555'
+);
+DELETE FROM market_trades WHERE source = 'p6_demo';
+
+-- ============================================================================
+-- Alice: five quarterly round trips, 3 profitable / 2 losing (60% consistency)
+-- ============================================================================
+INSERT INTO transactions (user_id, type, amount, currency, created_at, description) VALUES
+    ('b1111111-1111-1111-1111-111111111111', 'buy',  -5000.0000, 'USD', '2025-07-15 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'sell',  5800.0000, 'USD', '2025-07-20 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'fee',      -5.0000, 'USD', '2025-07-20 10:00', 'P6 demo data'),
+
+    ('b1111111-1111-1111-1111-111111111111', 'buy',  -4000.0000, 'USD', '2025-10-15 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'sell',  3500.0000, 'USD', '2025-10-20 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'fee',      -5.0000, 'USD', '2025-10-20 10:00', 'P6 demo data'),
+
+    ('b1111111-1111-1111-1111-111111111111', 'buy',  -6000.0000, 'USD', '2026-01-15 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'sell',  6700.0000, 'USD', '2026-01-20 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'fee',      -5.0000, 'USD', '2026-01-20 10:00', 'P6 demo data'),
+
+    ('b1111111-1111-1111-1111-111111111111', 'buy',  -3000.0000, 'USD', '2026-04-15 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'sell',  2600.0000, 'USD', '2026-04-20 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'fee',      -5.0000, 'USD', '2026-04-20 10:00', 'P6 demo data'),
+
+    ('b1111111-1111-1111-1111-111111111111', 'buy',  -4500.0000, 'USD', '2026-07-15 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'sell',  5200.0000, 'USD', '2026-07-20 10:00', 'P6 demo data'),
+    ('b1111111-1111-1111-1111-111111111111', 'fee',      -5.0000, 'USD', '2026-07-20 10:00', 'P6 demo data');
+
+-- ============================================================================
+-- Bob: three quarterly round trips, all profitable (100% consistency),
+-- smaller total P/L than Alice but a higher ROI.
+-- ============================================================================
+INSERT INTO transactions (user_id, type, amount, currency, created_at, description) VALUES
+    ('b2222222-2222-2222-2222-222222222222', 'buy',  -2000.0000, 'USD', '2025-10-10 10:00', 'P6 demo data'),
+    ('b2222222-2222-2222-2222-222222222222', 'sell',  2300.0000, 'USD', '2025-10-12 10:00', 'P6 demo data'),
+    ('b2222222-2222-2222-2222-222222222222', 'fee',      -3.0000, 'USD', '2025-10-12 10:00', 'P6 demo data'),
+
+    ('b2222222-2222-2222-2222-222222222222', 'buy',  -2500.0000, 'USD', '2026-01-10 10:00', 'P6 demo data'),
+    ('b2222222-2222-2222-2222-222222222222', 'sell',  2900.0000, 'USD', '2026-01-12 10:00', 'P6 demo data'),
+    ('b2222222-2222-2222-2222-222222222222', 'fee',      -3.0000, 'USD', '2026-01-12 10:00', 'P6 demo data'),
+
+    ('b2222222-2222-2222-2222-222222222222', 'buy',  -1800.0000, 'USD', '2026-04-10 10:00', 'P6 demo data'),
+    ('b2222222-2222-2222-2222-222222222222', 'sell',  2100.0000, 'USD', '2026-04-12 10:00', 'P6 demo data'),
+    ('b2222222-2222-2222-2222-222222222222', 'fee',      -3.0000, 'USD', '2026-04-12 10:00', 'P6 demo data');
+
+-- ============================================================================
+-- Market trades: BTC/USD trending up, ETH/USD trending down, five quarters.
+-- source='p6_demo' keeps these separate from data_load.sql's own rows and
+-- from live user/bot fills so this script can clean up after itself.
+-- ============================================================================
+INSERT INTO market_trades (market_id, executed_at, price, quantity, side, source) VALUES
+    ('a1111111-1111-1111-1111-111111111111', '2025-07-15 10:00', 40000.000000, 0.500000, 'buy',  'p6_demo'),
+    ('a1111111-1111-1111-1111-111111111111', '2025-10-15 10:00', 45000.000000, 0.800000, 'buy',  'p6_demo'),
+    ('a1111111-1111-1111-1111-111111111111', '2026-01-15 10:00', 55000.000000, 1.200000, 'buy',  'p6_demo'),
+    ('a1111111-1111-1111-1111-111111111111', '2026-04-15 10:00', 60000.000000, 1.000000, 'buy',  'p6_demo'),
+
+    ('a2222222-2222-2222-2222-222222222222', '2025-07-15 10:00',  4000.000000, 3.000000, 'sell', 'p6_demo'),
+    ('a2222222-2222-2222-2222-222222222222', '2025-10-15 10:00',  3800.000000, 2.500000, 'sell', 'p6_demo'),
+    ('a2222222-2222-2222-2222-222222222222', '2026-01-15 10:00',  3600.000000, 2.000000, 'sell', 'p6_demo'),
+    ('a2222222-2222-2222-2222-222222222222', '2026-04-15 10:00',  3550.000000, 1.800000, 'sell', 'p6_demo');
+
+-- ============================================================================
+-- Executed orders: who participated in which market, across the same quarters.
+-- ============================================================================
+INSERT INTO orders (id, user_id, market_id, side, type, status, quantity, price, placed_at, executed_at) VALUES
+    ('e1111111-1111-1111-1111-111111111111', 'b1111111-1111-1111-1111-111111111111',
+     'a1111111-1111-1111-1111-111111111111', 'buy', 'market', 'executed', 0.5000, 40000.000000,
+     '2025-07-15 10:00', '2025-07-15 10:00'),
+    ('e2222222-2222-2222-2222-222222222222', 'b1111111-1111-1111-1111-111111111111',
+     'a2222222-2222-2222-2222-222222222222', 'sell', 'market', 'executed', 3.0000, 4000.000000,
+     '2025-10-15 10:00', '2025-10-15 10:00'),
+    ('e3333333-3333-3333-3333-333333333333', 'b2222222-2222-2222-2222-222222222222',
+     'a1111111-1111-1111-1111-111111111111', 'buy', 'market', 'executed', 1.2000, 55000.000000,
+     '2026-01-15 10:00', '2026-01-15 10:00'),
+    ('e4444444-4444-4444-4444-444444444444', 'b2222222-2222-2222-2222-222222222222',
+     'a1111111-1111-1111-1111-111111111111', 'buy', 'market', 'executed', 1.0000, 60000.000000,
+     '2026-04-15 10:00', '2026-04-15 10:00'),
+    ('e5555555-5555-5555-5555-555555555555', 'b3333333-3333-3333-3333-333333333333',
+     'a2222222-2222-2222-2222-222222222222', 'sell', 'market', 'executed', 2.0000, 3600.000000,
+     '2026-01-15 10:00', '2026-01-15 10:00');
+}}}
+
+ * Ran both reports against a live PostgreSQL 16 database with that demo data loaded, through
+   the actual CLI, and used the real output (including a run where alice's seeded quarter
+   interacted with a pre-existing `data_load.sql` transaction and flipped a profitable quarter
+   into a loss) as the verified evidence in !AdvancedReports, rather
+   than inventing example numbers.
+
+== Summary of AI involvement ==
+
+||  ||= This session — 2026-09-16 =||
+||= What I brought =|| The phase rubric, plus both report questions fully specified down to the exact aggregate formulas ||
+||= What the AI did =|| Wrote the SQL, wrote the relational algebra, wired the reports into the CLI, designed and ran the demonstration data, verified everything against a live database ||
+||= What I decided =|| To keep both reports as SQL functions rather than plain ad-hoc queries so they are actually usable from the application; to accept the AI's synthetic multi-quarter demo dataset rather than wait for enough real usage history to accumulate ||
+
+The two ideas and their formulas were mine, specified in enough detail (P/L as the sum of
+buy+sell+fee transactions, ROI relative to total buys, consistency as a share of profitable
+quarters, market return as first-vs-last trade price, volatility as price standard deviation,
+participation from orders rather than trades) that there was no separate "AI alternative
+idea" to borrow from and document a change against, unlike the more open-ended P1–P3 phases —
+the AI's job here was implementation and verification of a fully-specified design, which is
+what is logged above and in the prompt below.
+
+== Entire AI usage log ==
+
+=== 2026-09-16 ===
+
+'''Intent:''' hand over the P6 rubric together with both report ideas, fully specified, and
+have the whole phase — SQL, relational algebra, prototype integration, and demonstration data
+— produced and verified in one pass.
+
+'''Prompt (student, verbatim):'''
+> Phase P6: Complex DB Reports (SQL, Stored Procedures, Relational Algebra)
+> [the full phase rubric was pasted: 2 complex analytical reports solvable each with one SQL
+> query, usable as reports within the application, with a note that helper views/functions/
+> procedures are acceptable when pure SQL isn't enough, that the design should be extended if
+> a good idea needs data that doesn't exist yet, a requirement for the corresponding relational
+> algebra, the exact `AdvancedReports`/`AdvancedReportsAIUsage` wiki templates, and the phase's
+> AI-use rules]
+>
+> I will describe for you the 2 more complex sql queries:
+> 1. Find the users who performed the best in a selected quarter, year, or multi-year period
+>    based on their realized trading performance, and show their total profit/loss, total
+>    amount invested, ROI, number of profitable and losing periods, and consistency of
+>    returns. Calculate realized P/L as SUM(sell transactions + buy transactions + fee
+>    transactions), calculate ROI as realized P/L / absolute value of total buy transactions ×
+>    100, and calculate consistency as profitable periods / total periods × 100.
+> 2. Find which cryptocurrency markets performed best during a selected quarter, year, or
+>    multi-year period based on trading activity and price behavior, and report the markets
+>    with the highest trading volume, number of trades, number of participating users, and
+>    price return. Calculate total volume as SUM(trade quantity), average trading price as
+>    AVG(price), market return as (last trade price − first trade price) / first trade price ×
+>    100, price volatility as standard deviation of trade prices, and user participation as
+>    COUNT(DISTINCT user_id) from the related executed orders.
+
+'''Response (AI, summarised):'''
+ * Confirmed both report questions were answerable from the existing schema with no new
+   columns or tables, and said so explicitly rather than silently skipping that check.
+ * Wrote `report_top_traders` and `report_market_performance` as SQL functions, added to
+   `schema_creation.sql` under a new `REPORTS (P6)` section.
+ * Wrote `server/reports.go` (`ShowTopTraders`, `ShowMarketPerformance`, a shared
+   `promptPeriod` helper) and added menu options `[10]`/`[11]` to `server/cli.go`.
+ * Wrote `server/db/reports_demo_data.sql` for a richer, multi-quarter demonstration, and
+   documented in its own header exactly why it is optional and what it deliberately does not
+   touch (user balances, holdings).
+ * Built the prototype, ran both reports against the live database with the demo data loaded,
+   cross-checked the SQL output against hand-computed expected values for both seeded users,
+   and used that real output as the verified evidence.
+ * Wrote out the relational-algebra derivation for each query and !AdvancedReports.
+
+'''What I decided:''' to keep the demo dataset out of `-init`/`-load-data` rather than merge it
+into `data_load.sql`, since the other phases' documented expected values (specific balances in
+!BuildInstructions) depend on the seed data staying
+exactly as it is.
+
+> '''Student action required.''' Read !AdvancedReports end to end
+> before the defense, and be ready to compute one period's realized P/L or one market's return
+> by hand from the raw `transactions`/`market_trades` rows — the numbers in the verified run
+> are real output, not invented, so they can be checked against
+> `reports_demo_data.sql` (shown above) directly. Append any further
+> prompts here if you ask for revisions.
+
+=== Follow-up — 2026-09-17 ===
+
+'''Intent:''' simplify the market performance report after seeing it run — with only a handful
+of trades per market in most periods, price volatility read as noise rather than a useful
+signal.
+
+'''Prompt (student, verbatim):'''
+> Can you tell me the exact database_url so I can execute the schema creating and the data_load?
+>
+> From the Market Performance can we remove Volatility, I don't think that is that useful, also
+> can you tell me what is users column?
+
+'''Response (AI, summarised):'''
+ * Gave the local dev connection string (`postgresql://bp_project:1234@localhost:5433/bp_database`,
+   from `.env`) and explained `participating_users` is `COUNT(DISTINCT user_id)` from that
+   market's executed orders, not from `market_trades` (which has no `user_id`).
+ * Removed `price_volatility`/`STDDEV(price)` from `report_market_performance()` in
+   `schema_creation.sql`, from its `RETURNS TABLE` and its `SELECT`, and from the corresponding
+   column in `server/reports.go`'s `ShowMarketPerformance`.
+ * Updated the relational algebra (`Stats`, `Result`) and the SQL listing, verified run output,
+   and prose in !AdvancedReports to match, and added a short note
+   explaining why the column was dropped.
+ * Rebuilt, re-ran `-init` and `reports_demo_data.sql` against the live database, and
+   re-verified the market performance report through the actual CLI before and after the
+   change to confirm only the one column disappeared and every other number is unchanged.
+
+'''What I decided:''' to drop the column entirely rather than keep it computed-but-hidden,
+since an unused computation left in the query is exactly the kind of thing that should not
+survive a review.