Update financial indicator recipes to use TICK syntax

Replace legacy dateadd patterns with TICK syntax in 11 recipes:
- @lookback := dateadd('M', -1, now()) → @lookback := '$now - 1M..$now'
- timestamp > @lookback → timestamp IN @lookback

Updated: RSI, MACD, ATR, Stochastic, Donchian Channels, Keltner Channels,
OBV, Rate of Change, Realized Volatility, Maximum Drawdown, Bid-Ask Spread

Also adds elapsed-time recipe to sidebar.

Author: javier

documentation/cookbook/sql/finance/atr.md

@@ -15,7 +15,7 @@ You want to measure volatility to set appropriate stop-losses or position sizes.
 ```questdb-sql demo title="Calculate 14-period ATR"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 WITH with_prev AS (
   SELECT
@@ -27,7 +27,7 @@ WITH with_prev AS (
     lag(close) OVER (PARTITION BY symbol ORDER BY timestamp) AS prev_close
   FROM market_data_ohlc_15m
   WHERE symbol = @symbol
-    AND timestamp > @lookback
+    AND timestamp IN @lookback
 ),
 true_range AS (
   SELECT
@@ -83,6 +83,10 @@ entry_price - 2 * atr AS stop_loss
 (account_size * 0.01) / atr AS position_size
 ```
 
+:::note EMA vs Wilder's smoothing
+This recipe uses standard EMA smoothing via `avg(value, 'period', 14)` where α = 2/(N+1). Wilder's original ATR uses α = 1/N, which is more gradual. For exact Wilder smoothing with a 14-period lookback, use `avg(value, 'period', 27)`. Most modern platforms offer both variants.
+:::
+
 :::info Related documentation
 - [EMA window function](/docs/query/functions/window-functions/reference/#avg)
 - [lag() function](/docs/query/functions/window-functions/reference/#lag)

documentation/cookbook/sql/finance/bid-ask-spread.md

@@ -15,7 +15,7 @@ You want to measure market liquidity and transaction costs. Narrow spreads indic
 ```questdb-sql demo title="Calculate bid-ask spread metrics"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('h', -1, now())
+  @lookback := '$now - 1h..$now'
 
 SELECT
   timestamp,
@@ -27,7 +27,7 @@ SELECT
   round((bid_price + ask_price) / 2, 5) AS mid_price
 FROM core_price
 WHERE symbol = @symbol
-  AND timestamp > @lookback
+  AND timestamp IN @lookback
 ORDER BY timestamp;
 ```
 
@@ -41,7 +41,7 @@ The query calculates:
 ```questdb-sql demo title="Average spread by time period"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('d', -1, now())
+  @lookback := '$now - 1d..$now'
 
 SELECT
   timestamp,
@@ -52,7 +52,7 @@ SELECT
   count() AS quote_count
 FROM core_price
 WHERE symbol = @symbol
-  AND timestamp > @lookback
+  AND timestamp IN @lookback
 SAMPLE BY 1h
 ORDER BY timestamp;
 ```

documentation/cookbook/sql/finance/donchian-channels.md

@@ -15,7 +15,7 @@ You want to identify breakout levels and trading ranges. Moving averages smooth
 ```questdb-sql demo title="Calculate 20-period Donchian Channels"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 SELECT
   timestamp,
@@ -42,7 +42,7 @@ SELECT
   )) / 2, 5) AS middle_channel
 FROM market_data_ohlc_15m
 WHERE symbol = @symbol
-  AND timestamp > @lookback
+  AND timestamp IN @lookback
 ORDER BY timestamp;
 ```
 

documentation/cookbook/sql/finance/keltner-channels.md

@@ -12,10 +12,10 @@ You want volatility bands that adapt to market conditions but are smoother than
 
 ## Solution
 
-```questdb-sql demo title="Calculate Keltner Channels with 20 EMA and 2x ATR"
+```questdb-sql demo title="Calculate Keltner Channels (20-period EMA ± 2× ATR)"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 WITH with_prev AS (
   SELECT
@@ -27,7 +27,7 @@ WITH with_prev AS (
     lag(close) OVER (PARTITION BY symbol ORDER BY timestamp) AS prev_close
   FROM market_data_ohlc_15m
   WHERE symbol = @symbol
-    AND timestamp > @lookback
+    AND timestamp IN @lookback
 ),
 with_tr AS (
   SELECT
@@ -50,7 +50,7 @@ with_indicators AS (
     symbol,
     close,
     avg(close, 'period', 20) OVER (PARTITION BY symbol ORDER BY timestamp) AS ema20,
-    avg(tr, 'period', 10) OVER (PARTITION BY symbol ORDER BY timestamp) AS atr
+    avg(tr, 'period', 20) OVER (PARTITION BY symbol ORDER BY timestamp) AS atr
   FROM with_tr
 )
 SELECT
@@ -66,7 +66,7 @@ ORDER BY timestamp;
 
 The query:
 1. Calculates True Range accounting for gaps
-2. Applies EMA smoothing to both price (20-period) and ATR (10-period)
+2. Applies 20-period EMA smoothing to both price and ATR
 3. Creates bands at ±2 ATR from the EMA
 
 ## Interpreting results

documentation/cookbook/sql/finance/macd.md

@@ -15,7 +15,7 @@ You want to identify trend changes and momentum shifts. Simple moving averages l
 ```questdb-sql demo title="Calculate MACD with signal line and histogram"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 WITH ema AS (
   SELECT
@@ -26,7 +26,7 @@ WITH ema AS (
     avg(close, 'period', 26) OVER (PARTITION BY symbol ORDER BY timestamp) AS ema26
   FROM market_data_ohlc_15m
   WHERE symbol = @symbol
-    AND timestamp > @lookback
+    AND timestamp IN @lookback
 ),
 macd_line AS (
   SELECT

documentation/cookbook/sql/finance/maximum-drawdown.md

@@ -15,7 +15,7 @@ You want to measure downside risk beyond simple volatility. Standard deviation t
 ```questdb-sql demo title="Calculate rolling maximum drawdown"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 WITH with_peak AS (
   SELECT
@@ -29,7 +29,7 @@ WITH with_peak AS (
     ) AS running_peak
   FROM market_data_ohlc_15m
   WHERE symbol = @symbol
-    AND timestamp > @lookback
+    AND timestamp IN @lookback
 ),
 with_drawdown AS (
   SELECT

documentation/cookbook/sql/finance/obv.md

@@ -15,22 +15,22 @@ You want to confirm price trends with volume or spot divergences where volume do
 ```questdb-sql demo title="Calculate On-Balance Volume"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 WITH with_direction AS (
   SELECT
     timestamp,
     symbol,
     close,
-    quantity AS volume,
+    total_volume AS volume,
     CASE
-      WHEN close > lag(close) OVER (PARTITION BY symbol ORDER BY timestamp) THEN quantity
-      WHEN close < lag(close) OVER (PARTITION BY symbol ORDER BY timestamp) THEN -quantity
+      WHEN close > lag(close) OVER (PARTITION BY symbol ORDER BY timestamp) THEN total_volume
+      WHEN close < lag(close) OVER (PARTITION BY symbol ORDER BY timestamp) THEN -total_volume
       ELSE 0
     END AS directed_volume
   FROM fx_trades_ohlc_1m
   WHERE symbol = @symbol
-    AND timestamp > @lookback
+    AND timestamp IN @lookback
 )
 SELECT
   timestamp,

documentation/cookbook/sql/finance/rate-of-change.md

@@ -15,7 +15,7 @@ You want a simple momentum indicator that shows how fast price is changing. Raw
 ```questdb-sql demo title="Calculate 12-period Rate of Change"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 SELECT
   timestamp,
@@ -29,7 +29,7 @@ SELECT
   ) AS roc
 FROM market_data_ohlc_15m
 WHERE symbol = @symbol
-  AND timestamp > @lookback
+  AND timestamp IN @lookback
 ORDER BY timestamp;
 ```
 

documentation/cookbook/sql/finance/realized-volatility.md

@@ -15,7 +15,7 @@ You want to measure how volatile an asset has actually been, either for risk man
 ```questdb-sql demo title="Calculate 20-period realized volatility (annualized)"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 WITH returns AS (
   SELECT
@@ -25,7 +25,7 @@ WITH returns AS (
     ln(close / lag(close) OVER (PARTITION BY symbol ORDER BY timestamp)) AS log_return
   FROM market_data_ohlc_15m
   WHERE symbol = @symbol
-    AND timestamp > @lookback
+    AND timestamp IN @lookback
 ),
 with_stats AS (
   SELECT
@@ -51,15 +51,15 @@ SELECT
   symbol,
   round(close, 5) AS close,
   round(log_return * 100, 4) AS return_pct,
-  round(sqrt(mean_sq_return - mean_return * mean_return) * sqrt(252 * 96) * 100, 2) AS realized_vol_annualized
+  round(sqrt(mean_sq_return - mean_return * mean_return) * sqrt(365 * 96) * 100, 2) AS realized_vol_annualized
 FROM with_stats
 ORDER BY timestamp;
 ```
 
 The query:
 1. Calculates log returns: `ln(close / previous_close)`
 2. Computes rolling standard deviation using variance formula
-3. Annualizes by multiplying by `sqrt(trading_periods_per_year)` (252 days × 96 fifteen-minute periods = 24,192)
+3. Annualizes by multiplying by `sqrt(periods_per_year)` (365 days × 96 fifteen-minute periods = 35,040 for the 24/7 simulated data)
 
 ## Interpreting results
 
@@ -69,8 +69,7 @@ The query:
 - **Realized < Implied**: Options may be expensive
 
 :::note Annualization factor
-For 15-minute data with 24/7 trading: `sqrt(365 * 96) ≈ 187`
-For daily data with ~252 trading days: `sqrt(252) ≈ 15.87`
+The demo FX data is simulated continuously (24/7, including weekends), so the annualization factor uses `365 * 96` (365 days × 96 fifteen-minute periods per day). For real FX markets (24/5), use `252 * 96`. For daily data, use `sqrt(252) ≈ 15.87`.
 :::
 
 :::info Related documentation

documentation/cookbook/sql/finance/rsi.md

@@ -15,7 +15,7 @@ You want to identify when an asset may be overbought or oversold based on recent
 ```questdb-sql demo title="Calculate 14-period RSI with EMA smoothing"
 DECLARE
   @symbol := 'EURUSD',
-  @lookback := dateadd('M', -1, now())
+  @lookback := '$now - 1M..$now'
 
 WITH changes AS (
   SELECT
@@ -25,7 +25,7 @@ WITH changes AS (
     close - lag(close) OVER (PARTITION BY symbol ORDER BY timestamp) AS change
   FROM market_data_ohlc_15m
   WHERE symbol = @symbol
-    AND timestamp > @lookback
+    AND timestamp IN @lookback
 ),
 gains_losses AS (
   SELECT
@@ -68,8 +68,8 @@ The query:
 - **RSI = 50**: Neutral momentum
 - **Divergence**: When price makes new highs but RSI doesn't, it may signal weakening momentum
 
-:::note RSI smoothing
-Traditional RSI uses Wilder's smoothing (equivalent to EMA with period 2N-1). The `avg(value, 'period', 14)` function uses standard EMA where α = 2/(14+1). For exact Wilder smoothing, use `avg(value, 'period', 27)` for a 14-period RSI.
+:::note EMA vs Wilder's smoothing
+This recipe uses standard EMA smoothing via `avg(value, 'period', 14)` where α = 2/(N+1). Traditional RSI (Wilder's) uses α = 1/N, which is more gradual. For exact Wilder smoothing with a 14-period lookback, use `avg(value, 'period', 27)`. Most modern platforms offer both variants.
 :::
 
 :::info Related documentation