## Few-Shot Examples **User:** "What was the total profit last month?" **Agent:** **Reasoning:** Calculating PnL requires the history table. "Last month" requires ClickHouse date filtering relative to now. Filtering based on closeTime for optimal partition pruning. ```sql SELECT toStartOfMonth(closeTime) as month, SUM(tradingNetProfit) as total_net_profit, SUM(tradingGrossProfit) as total_gross_profit FROM close_trades_history_details_mv FINAL WHERE closeTime >= toStartOfMonth(now() - INTERVAL 1 MONTH) AND closeTime < toStartOfMonth(now()) GROUP BY month ORDER BY month DESC; ``` **User:** "List the top 5 instruments by volume." **Agent:** **Reasoning:** "Volume" is ambiguous. I will use `closeAmount` (Lots) as the metric from the history table. I am assuming a 30-day time window for performance. Filtering based on closeTime for optimal partition pruning. ```sql SELECT instrument, SUM(closeAmount) as total_lots_traded FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY instrument ORDER BY total_lots_traded DESC LIMIT 5; ``` **User:** "Show me all actions for trade ID 500." **Agent:** **Reasoning:** The user asked for "actions," which implies an audit log. I will use the `trades_summary` table. Including trade metadata (tradeid, orderid, instrumentname, side, lots) as required. ```sql SELECT tradeid, orderid, time, instrumentname, isbuy, iscloseorder, tradeprice, tradeamount FROM trades_summary FINAL WHERE tradeid = 500 ORDER BY time ASC ; ``` **User:** "Find me users whose average trade lasted less than a minute." **Agent:** **Reasoning:** I need to calculate the duration (`closeTime` - `openTime`). I will filter for the last 30 days based on closeTime for optimal partition pruning. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.avg_duration_seconds, agg.total_trades FROM ( SELECT accountId, avg(dateDiff('second', openTime, closeTime)) as avg_duration_seconds, count() as total_trades FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId HAVING avg_duration_seconds < 60 ) agg JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY agg.avg_duration_seconds ASC ; ``` **User:** "Give me the top 10 percent of the most profitable traders and their pnl." **Agent:** **Reasoning:** To calculate the top 10% profitably, I will use a subquery to find the 90th percentile (`quantile(0.9)`) of total net profit. I selected this approach over window functions for three specific ClickHouse optimizations: 1. **Parallelism:** The `quantile` calculation is parallelized across all cores/nodes. 2. **Reduced Memory:** It doesn't need to keep a sorted list of every user in memory; it uses a statistical approximation. 3. **Push-down Predicate:** The `HAVING` clause filters out "losers" early, so the final `ORDER BY` deals with a smaller dataset. JOINing with account_summary to include all trader metadata (userid, accountid, email, firstname, lastname). ```sql SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.total_net_profit FROM ( SELECT accountId, SUM(tradingNetProfit) as total_net_profit FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId HAVING total_net_profit >= ( SELECT quantile(0.9)(total_profit) FROM ( SELECT SUM(tradingNetProfit) as total_profit FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId ) ) ) agg JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY agg.total_net_profit DESC ; ``` **User:** "Show me scalping trades held for less than 5 minutes in the last week." **Agent:** **Reasoning:** The user has specified scalping with a clear duration threshold (< 5 minutes). I will use `dateDiff('second', openTime, closeTime)` to filter trades by duration. Time filter based on closeTime (last 7 days) for optimal partition pruning. Including tradeId, positionId, and accountname as required metadata. ```sql SELECT tradeId, positionId, accountId, accountname, instrument, positionSide, openTime, closeTime, dateDiff('second', openTime, closeTime) as hold_duration_seconds, tradingNetProfit, closeAmount FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 7 DAY -- Filter on closeTime (partition key) AND dateDiff('second', openTime, closeTime) < 300 -- Less than 5 minutes (300 seconds) ORDER BY hold_duration_seconds ASC ; ``` **User:** "Find all day trading activity (same-day positions) for account 12345 in November 2024." **Agent:** **Reasoning:** Day trading means positions opened and closed on the same calendar day. I will use `toDate(openTime) = toDate(closeTime)` to identify same-day trades. Filtering for specific account and month based on closeTime for optimal partition pruning. Including positionId, tradeId, and accountname as required metadata. ```sql SELECT tradeId, positionId, accountId, accountname, instrument, positionSide, openTime, closeTime, dateDiff('hour', openTime, closeTime) as hold_duration_hours, tradingNetProfit, closeAmount FROM close_trades_history_details_mv FINAL WHERE accountId = '12345' AND closeTime >= '2024-11-01 00:00:00' -- Filter on closeTime (partition key) AND closeTime < '2024-12-01 00:00:00' AND toDate(openTime) = toDate(closeTime) -- Same day open and close ORDER BY closeTime DESC ; ``` **User:** "Identify accounts with swing trading patterns (positions held 3-7 days) in the last month." **Agent:** **Reasoning:** The user specified swing trading with a duration range of 3-7 days. I will calculate position duration in days using `dateDiff('day', openTime, closeTime)` and filter accordingly. Grouping by account to identify accounts with this pattern. Filtering based on closeTime for optimal partition pruning. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.swing_trades_count, agg.avg_hold_days, agg.total_profit_from_swing_trades, agg.avg_lot_size FROM ( SELECT accountId, count() as swing_trades_count, avg(dateDiff('day', openTime, closeTime)) as avg_hold_days, sum(tradingNetProfit) as total_profit_from_swing_trades, avg(closeAmount) as avg_lot_size FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY AND dateDiff('day', openTime, closeTime) BETWEEN 3 AND 7 GROUP BY accountId HAVING swing_trades_count > 0 ) agg JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY agg.swing_trades_count DESC ; ``` **User:** "Show me potential arbitrage activity - accounts with simultaneous BUY and SELL positions in the same instrument." **Agent:** **Reasoning:** Arbitrage involves opposing positions opened at nearly the same time. I will group trades by account, instrument, and minute-level time window to find simultaneous BUY and SELL activity. This pattern suggests hedging or arbitrage strategy. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.instrument, agg.minute_window, agg.buy_positions, agg.sell_positions, agg.net_profit_in_window, agg.total_positions FROM ( SELECT accountId, instrument, toStartOfMinute(openTime) as minute_window, countIf(positionSide = 'BUY') as buy_positions, countIf(positionSide = 'SELL') as sell_positions, sum(tradingNetProfit) as net_profit_in_window, count() as total_positions FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId, instrument, minute_window HAVING buy_positions > 0 AND sell_positions > 0 ) agg JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY agg.total_positions DESC ; ``` **User:** "Analyze position sizing consistency for account XYZ - show me lot size variance." **Agent:** **Reasoning:** Position sizing consistency is a risk indicator. I will calculate standard deviation and coefficient of variation of lot sizes. Low variance indicates disciplined, consistent sizing (conservative). High variance suggests inconsistent sizing (potentially aggressive/emotional trading). ```sql SELECT accountId, count() as total_trades, avg(closeAmount) as avg_lot_size, stddevPop(closeAmount) as lot_size_std_dev, COALESCE(stddevPop(closeAmount) / NULLIF(avg(closeAmount), 0), 0) as coefficient_of_variation, min(closeAmount) as min_lot_size, max(closeAmount) as max_lot_size, max(closeAmount) / NULLIF(min(closeAmount), 1) as max_min_ratio FROM close_trades_history_details_mv FINAL WHERE accountId = 'XYZ' AND closeTime >= now() - INTERVAL 30 DAY -- Filter on closeTime (partition key) GROUP BY accountId; ``` **User:** "What percentage of trades use stop-loss orders by account? Show top 20 accounts." **Agent:** **Reasoning:** Stop-loss usage indicates risk management discipline. I will calculate the percentage of trades with stop-loss values set. High SL usage suggests conservative, risk-aware trading. Low usage indicates higher risk tolerance or manual management style. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.total_trades, agg.trades_with_stoploss, agg.stoploss_usage_percent, agg.trades_with_takeprofit, agg.takeprofit_usage_percent FROM ( SELECT accountId, count() as total_trades, countIf(stoploss IS NOT NULL AND stoploss != 0) as trades_with_stoploss, COALESCE(countIf(stoploss IS NOT NULL AND stoploss != 0) * 100.0 / NULLIF(count(), 0), 0) as stoploss_usage_percent, countIf(tpPrice IS NOT NULL AND tpPrice != 0) as trades_with_takeprofit, COALESCE(countIf(tpPrice IS NOT NULL AND tpPrice != 0) * 100.0 / NULLIF(count(), 0), 0) as takeprofit_usage_percent FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId HAVING total_trades >= 10 ) agg JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY agg.stoploss_usage_percent DESC LIMIT 20; ``` **User:** "Show instrument diversification for each account - how many different instruments do they trade?" **Agent:** **Reasoning:** Instrument diversification is a risk indicator. Trading many instruments spreads risk (conservative), while concentrating in few instruments creates concentrated risk (aggressive). I will count unique instruments and calculate concentration metrics. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.total_trades, agg.unique_instruments_traded, agg.avg_trades_per_instrument FROM ( SELECT accountId, count() as total_trades, uniqExact(instrument) as unique_instruments_traded, COALESCE(count() / NULLIF(uniqExact(instrument), 0), 0) as avg_trades_per_instrument FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId HAVING total_trades >= 10 ) agg JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY agg.unique_instruments_traded DESC ; ``` **User:** "Calculate win rate and risk-reward ratio for all accounts with at least 20 trades." **Agent:** **Reasoning:** Win rate and risk-reward ratio are key performance metrics. Win rate = % of profitable trades. Risk-reward ratio = average win / average loss. These metrics together reveal trading effectiveness and risk management quality. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.total_trades, agg.winning_trades, agg.losing_trades, agg.win_rate_percent, agg.avg_win_amount, agg.avg_loss_amount, agg.risk_reward_ratio, agg.total_net_profit FROM ( SELECT accountId, count() as total_trades, countIf(tradingNetProfit > 0) as winning_trades, countIf(tradingNetProfit <= 0) as losing_trades, COALESCE(countIf(tradingNetProfit > 0) * 100.0 / NULLIF(count(), 0), 0) as win_rate_percent, COALESCE(sumIf(tradingNetProfit, tradingNetProfit > 0) / NULLIF(countIf(tradingNetProfit > 0), 0), 0) as avg_win_amount, abs(COALESCE(sumIf(tradingNetProfit, tradingNetProfit < 0) / NULLIF(countIf(tradingNetProfit < 0), 0), 0)) as avg_loss_amount, COALESCE( (sumIf(tradingNetProfit, tradingNetProfit > 0) / NULLIF(countIf(tradingNetProfit > 0), 0)) / NULLIF(abs(sumIf(tradingNetProfit, tradingNetProfit < 0) / NULLIF(countIf(tradingNetProfit < 0), 0)), 0), 0 ) as risk_reward_ratio, sum(tradingNetProfit) as total_net_profit FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId HAVING total_trades >= 20 ) agg JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY agg.total_net_profit DESC ; ``` **User:** "Which accounts hold positions overnight frequently? Show overnight trading percentage." **Agent:** **Reasoning:** Overnight positions indicate swap cost tolerance and gap risk acceptance. High overnight % suggests swing/position trading style and acceptance of overnight risk. Low overnight % suggests day trading style or risk aversion to gap risk. Using `toDate()` comparison to identify overnight positions. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.total_trades, agg.overnight_positions, agg.overnight_percent, agg.total_swap_costs, agg.avg_swap_per_trade, agg.total_net_profit FROM ( SELECT accountId, count() as total_trades, countIf(toDate(openTime) != toDate(closeTime)) as overnight_positions, COALESCE(countIf(toDate(openTime) != toDate(closeTime)) * 100.0 / NULLIF(count(), 0), 0) as overnight_percent, sum(swapsApplied) as total_swap_costs, avg(swapsApplied) as avg_swap_per_trade, sum(tradingNetProfit) as total_net_profit FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId HAVING total_trades >= 10 ) agg JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY agg.overnight_percent DESC ; ``` **User:** "What's the total profit across all accounts in the last 30 days?" **Agent:** **Reasoning:** This is a cross-account aggregation (summing across ALL accounts). Since we're NOT grouping by accountId, we should NOT include account-specific metadata like accountname. We GROUP BY accountcurrency to avoid meaningless cross-currency aggregation — each currency gets its own total. ```sql SELECT accountcurrency, SUM(tradingNetProfit) AS total_net_profit, COUNT(DISTINCT accountId) AS num_accounts, COUNT(*) AS total_trades FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY AND closeTime <= now() GROUP BY accountcurrency -- Note: No GROUP BY accountId, so no accountname included ``` **User:** "Show me net profit by account in the last 30 days, top 10." **Agent:** **Reasoning:** This is a per-account aggregation (grouping BY accountId). Since we're grouping by account, we include all trader metadata for human readability. We also GROUP BY accountcurrency so that ranking is done within each currency group — comparing profit across different currencies is meaningless. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, ranked.accountId, acs.email, acs.firstname, acs.lastname, ranked.accountcurrency, ranked.total_net_profit, ranked.total_trades FROM ( SELECT accountId, accountcurrency, SUM(tradingNetProfit) AS total_net_profit, COUNT(*) AS total_trades, row_number() OVER (PARTITION BY accountcurrency ORDER BY total_net_profit DESC) AS rn FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY AND closeTime <= now() GROUP BY accountId, accountcurrency ) ranked JOIN account_summary acs FINAL ON toString(ranked.accountId) = toString(acs.accountid) WHERE ranked.rn <= 10 ORDER BY ranked.accountcurrency, ranked.total_net_profit DESC; ``` **User:** "Return all time net deposits for accounts." **Agent:** **Reasoning:** Net deposits = Deposits - Withdrawals. Using `accountoperation` table where operationtypeid 2 = Deposit and 3 = Withdraw (already stored as negative). IMPORTANT: Credits (operationtypeid=19) are excluded because they are promotional/bonus funds, not real money deposits. Since we're aggregating per account (not across accounts), currency handling is implicit - each account has exactly one currency, so no cross-currency summing occurs. The account's currency is simply a descriptive attribute. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountid, acs.email, acs.firstname, acs.lastname, agg.total_deposits, agg.total_withdrawals, agg.net_deposits FROM ( SELECT accountid, SUM(CASE WHEN operationtypeid = 2 THEN sum ELSE 0 END) AS total_deposits, SUM(CASE WHEN operationtypeid = 3 THEN sum ELSE 0 END) AS total_withdrawals, SUM(sum) AS net_deposits FROM accountoperation FINAL WHERE operationtypeid IN (2, 3) GROUP BY accountid ) agg JOIN account_summary acs FINAL ON agg.accountid = acs.accountid ORDER BY agg.net_deposits DESC ; ``` **User:** "Show me total credits received by each account." **Agent:** **Reasoning:** The user specifically asked for "credits", so NOW we include operationtypeid=19. Credits are promotional/bonus funds separate from deposits/withdrawals. We filter to only operationtypeid=19 to show credit activity exclusively. Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountid, acs.email, acs.firstname, acs.lastname, agg.total_credits, agg.credit_count, agg.first_credit_date, agg.last_credit_date FROM ( SELECT accountid, SUM(sum) AS total_credits, COUNT(*) AS credit_count, MIN(createdat) AS first_credit_date, MAX(createdat) AS last_credit_date FROM accountoperation FINAL WHERE operationtypeid = 19 GROUP BY accountid ) agg JOIN account_summary acs FINAL ON agg.accountid = acs.accountid ORDER BY agg.total_credits DESC ``` -- Note: Credits are ONLY included when user specifically mentions "credits" or "bonuses" **User:** "Show me all currently open positions for account 12345." **Agent:** **Reasoning:** Open positions are derived from `trades_summary` via the `positionamount` field (size after each trade). Because positions can only shrink in our system, `argMax(positionamount, time)` per `positionid` gives the current remaining size — `> 0` means still open. I do NOT apply a time filter on trades: a position can have been opened well before any analysis window, and dropping older trades would misclassify already-closed positions as open. Aggregating per position first, then enriching with trader metadata from `account_summary`. ```sql SELECT acs.userid, agg.accountid, acs.email, acs.firstname, acs.lastname, agg.positionid, agg.instrumentname, agg.remaining_amount, agg.opened_at FROM ( SELECT accountid, positionid, any(instrumentname) AS instrumentname, min(positionamount) AS remaining_amount, min(if(iscloseorder = 0, time, NULL)) AS opened_at FROM trades_summary FINAL WHERE accountid = 12345 GROUP BY accountid, positionid HAVING remaining_amount > 0 ) agg JOIN account_summary acs FINAL ON toString(agg.accountid) = toString(acs.accountid) ORDER BY agg.opened_at DESC ; ``` **User:** "What's the total account funding including deposits and credits?" **Agent:** **Reasoning:** The user explicitly asked for BOTH "deposits" AND "credits", so we include both operationtypeid=2 (Deposit) and operationtypeid=19 (Credit). We show them separately for transparency since they represent different types of funding (real money vs promotional). Aggregating first, then enriching with trader metadata from account_summary. ```sql SELECT acs.userid, agg.accountid, acs.email, acs.firstname, acs.lastname, agg.total_deposits, agg.total_credits, agg.total_funding FROM ( SELECT accountid, SUM(CASE WHEN operationtypeid = 2 THEN sum ELSE 0 END) AS total_deposits, SUM(CASE WHEN operationtypeid = 19 THEN sum ELSE 0 END) AS total_credits, SUM(CASE WHEN operationtypeid IN (2, 19) THEN sum ELSE 0 END) AS total_funding FROM accountoperation FINAL WHERE operationtypeid IN (2, 19) GROUP BY accountid ) agg JOIN account_summary acs FINAL ON agg.accountid = acs.accountid ORDER BY agg.total_funding DESC ; ``` **User:** "Activation date for these HEROFX accounts: 1001, 1002, 1003, 1004." **Spec extras:** `user_supplied_data = [{ "name": "supplied_account_ids", "description": "Account IDs the user wants results restricted to.", "payload": "1001, 1002, 1003, 1004" }]` **Agent:** **Reasoning:** The user pinned the result set to a specific list of account IDs. I embed those literals as an `IN (...)` filter. The spec's `description` told me what the list represents; choosing the column (`accountid`) and the operator (`IN`) is my call. ```sql -- supplied_account_ids: filter to the user-pinned account list SELECT accountid, accountname, createdat AS activation_date, brandIdPrefix FROM account_summary FINAL WHERE accountid IN (1001, 1002, 1003, 1004) AND brandIdPrefix = upper('herofx') ORDER BY createdat ASC ; ``` **User:** "Show me account creation dates for these accounts: 509203, 510124, 511054 ..." **Spec extras:** `user_supplied_data = [{ "name": "supplied_account_ids", "description": "Newline-delimited account IDs.", "payload": "509203\n510124\n511054\n..." }]` **Agent:** **Reasoning:** Newline-delimited payload — expand into rows first, then cast per row. Lambdas (`x -> ...`) are not valid in ClickHouse scalar `WITH` expressions; use a subquery CTE with `arrayJoin` instead. Casting both sides to `toString` for safe type matching in the filter. ```sql WITH supplied_account_ids AS ( SELECT toDecimal64(trim(BOTH '\n' FROM id_str), 0) AS accountid FROM (SELECT arrayJoin(splitByChar('\n', '')) AS id_str) ) SELECT accountid, createdat AS account_creation_date FROM account_summary FINAL WHERE toString(accountid) IN (SELECT toString(accountid) FROM supplied_account_ids) ORDER BY accountid ASC; ``` **User:** "Convert net profit to USD using EUR=1.08, GBP=1.25, CHF=1.10 and rank top 10 accounts in the last 30 days." **Spec extras:** `user_supplied_data = [{ "name": "usd_rates", "description": "USD conversion rates keyed by source-currency code.", "payload": "EUR=1.08, GBP=1.25, CHF=1.10" }]` **Agent:** **Reasoning:** The user supplied currency→rate pairs. I build a small literal rates table via the `VALUES` table function and JOIN on `accountcurrency`. I include a `USD = 1.00` row so native-USD accounts aren't dropped by the inner JOIN. Aggregating per account first, then joining metadata. `accountId` is String on the trades MV but Decimal on `account_summary`, so I cast `acs.accountid` to String for the join. ```sql WITH usd_rates AS ( SELECT currency, toFloat64(rate) AS rate FROM VALUES ( 'currency String, rate Float64', ('EUR', 1.08), ('GBP', 1.25), ('CHF', 1.10), ('USD', 1.00) ) ) SELECT acs.userid, agg.accountId, acs.email, acs.firstname, acs.lastname, agg.accountcurrency, agg.total_net_profit_native, agg.total_net_profit_native * r.rate AS total_net_profit_usd FROM ( SELECT accountId, accountcurrency, SUM(tradingNetProfit) AS total_net_profit_native FROM close_trades_history_details_mv FINAL WHERE closeTime >= now() - INTERVAL 30 DAY GROUP BY accountId, accountcurrency ) agg JOIN usd_rates r ON r.currency = agg.accountcurrency JOIN account_summary acs FINAL ON toString(agg.accountId) = toString(acs.accountid) ORDER BY total_net_profit_usd DESC LIMIT 10 ; ```