daily fix

vbt-snippets.md

@@ -1,5 +1,6 @@
 - [FETCHING DATA](#fetching-data)
   - [REINDEX to main session](#reindex-to-main-session)
+  - [indexing](#indexing)
 - [DISCOVERY](#discovery)
 - [DATA/WRAPPER](#datawrapper)
   - [create WRAPPER manually](#create-wrapper-manually)
@@ -10,8 +11,8 @@
 - [SIGNALS](#signals)
   - [ENTRIES/EXITS time based](#entriesexits-time-based)
   - [STOPS](#stops)
-  - [OHLCSTX module](#ohlcstx-module)
-  - [ENTRY WINDOW and FORCED EXIT WINDOW](#entry-window-and-forced-exit-window)
+  - [OHLCSTX Module](#ohlcstx-module)
+  - [Entry Window and Forced Exit Window](#entry-window-and-forced-exit-window)
   - [END OF DAY EXITS](#end-of-day-exits)
   - [REGULAR EXITS](#regular-exits)
 - [DF/SR ACCESSORS](#dfsr-accessors)
@@ -95,7 +96,10 @@ testData = vbt.YFData.fetch(['MSFT'], start=start, end=end, timeframe=timeframe,
 # of market hours)
 testData = testData.transform(lambda x: x.reindex(market_klines))
 ```
-
+## indexing
+```python
+entries.vbt.xloc[slice("2024-08-01","2024-08-03")].obj.info()
+```
 # DISCOVERY
 
 ```python
@@ -235,8 +239,9 @@ mask.sum()
 ```python
 #create entries/exits based on open of first symbol
 entries = pd.DataFrame.vbt.signals.empty_like(data.open.iloc[:,0]) 
+exits = pd.DataFrame.vbt.signals.empty_like(entries)
 
-#create entries/exits based on symbol level
+#OR create entries/exits based on symbol level if needed (for each columns)
 symbol_wrapper = data.get_symbol_wrapper()
 entries = symbol_wrapper.fill(False)
 exits = symbol_wrapper.fill(False)
@@ -257,7 +262,6 @@ exits.vbt.set(
 )
 ```
 
-
 ## STOPS
 [doc from_signal](http://5.161.179.223:8000/vbt-doc/api/portfolio/base/#vectorbtpro.portfolio.base.Portfolio.from_signals) 
 
@@ -268,14 +272,16 @@ price = close.vbt.wrapper.fill()
 price[entries] = entry_price
 price[exits] = exit_price
 
-## OHLCSTX module
+## OHLCSTX Module
  - exit signal generator based on price and stop values
 [doc](ttp://5.161.179.223:8000/vbt-doc/api/signals/generators/ohlcstx/index.html)
 
 
-## ENTRY WINDOW and FORCED EXIT WINDOW
+## Entry Window and Forced Exit Window
 Applying `entry window `range (denoted by minutes from the session start) to `entries` and applying `forced exit window` to `exits`.
 
+`create_mask_from_window` with param `use_cal=True` (default) uses market calendar data for each day to denote session start and end. When disabled it uses just fixed 9:30-16:00 for each day.
+
 ```python
 from ttools import create_mask_from_window
 
@@ -285,12 +291,12 @@ forced_exit_start = 387
 forced_exit_end = 390
 
 #create mask based on main session that day
-entry_window_opened = create_mask_from_window(entries, entry_window_opens, entry_window_closes)
+entry_window_opened = create_mask_from_window(entries, entry_window_opens, entry_window_closes, use_cal=True)
 #limit entries to the window
 entries = entries & entry_window_opened
 
 #create forced exits mask
-forced_exits_window = create_mask_from_window(exits, forced_exit_start, forced_exit_end)
+forced_exits_window = create_mask_from_window(exits, forced_exit_start, forced_exit_end, use_cal=True)
 
 #add forced_exits to exits
 exits = exits | forced_exits_window
@@ -309,6 +315,10 @@ exits = t1data.get_symbol_wrapper().fill(False)
 exits.loc[last_n_daily_rows.index] = True
 #visualize
 t1data.ohlcv.data["BAC"].lw.plot(right=[(t1data.close,"close",exits)], size="s")
+
+#which is ALTERNATIVE to
+exits = create_mask_from_window(t1data.close, 387, 390, use_cal=False)
+t1data.ohlcv.data["BAC"].lw.plot(right=[(t1data.close,"close",exits)], size="s")
 ```
 ## REGULAR EXITS
 Time based.
@@ -327,11 +337,11 @@ exits.vbt.set(
 # DF/SR ACCESSORS 
 
 ## Generic
-- for common taks ([docs](http://5.161.179.223:8000/vbt-doc/api/generic/accessors/index.html#vectorbtpro.generic.accessors.GenericAccessor)) 
+For common taks ([docs](http://5.161.179.223:8000/vbt-doc/api/generic/accessors/index.html#vectorbtpro.generic.accessors.GenericAccessor)) 
 
- `rolling_apply` - runs custom function over a rolling window of a fixed size (number of bars or frequency)
+*  `rolling_apply` - runs custom function over a rolling window of a fixed size (number of bars or frequency)
 
-`expanding_apply` - runs custome function over expanding the window from the start of the data to the current poin
+* `expanding_apply` - runs custome function over expanding the window from the start of the data to the current poin
 
 ```python
 from numba import njit
@@ -340,6 +350,7 @@ hourly_anchored_expanding_mean = t1data.close.vbt.rolling_apply("1H", mean_nb) #
 t1data.ohlcv.data["BAC"].lw.plot(right=[(t1data.close,"close"),(hourly_anchored_expanding_mean, "hourly_anchored_expanding_mean")], size="s")
 #NOTE for anchored "1D" frequency - it measures timedelta that means requires 1 day between reseting (16:00 end of market, 9:30 start - not a full day, so it is enOugh to set 7H)
 
+#HEATMAP OVERLAY
 df['a'].vbt.overlay_with_heatmap(df['b']).show()
 ```
 
@@ -523,6 +534,7 @@ t1data.ohlcv.data["BAC"].lw.plot(auto_scale=[mom_anch_d, mom])
 ```python
 #SPLITTER - splitting wrapper based on index
 #http://5.161.179.223:8000/vbt-doc/tutorials/cross-validation/splitter/index.html#anchored
+#based on GROUPER
 daily_splitter = vbt.Splitter.from_grouper(t1data.index, "D", split=None) #DOES contain last DAY
 
 daily_splitter = vbt.Splitter.from_ranges( #doesnt contain last DY
@@ -533,6 +545,9 @@ daily_splitter = vbt.Splitter.from_ranges( #doesnt contain last DY
 daily_splitter.stats()
 daily_splitter.plot()
 daily_splitter.coverage()
+daily_splitter.get_bounds(index_bounds=True) #shows the exact times
+daily_splitter.get_bounds_arr()
+daily_splitter.get_range_coverage(relative=True)
 
 #TAKING and APPLY MANUALLY - run UDF on ALL takes and concatenates
 taken = daily_splitter.take(t1data)