Create testdoc.md (#204)

testdoc.md

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+# Plotly
+
+* MAKE_SUBPLOT Defines layout (if more then 1x1 or secondary y axis are required)
+
+```python
+fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, 
+                        specs=[[{"secondary_y": True}], [{"secondary_y": False}]], 
+                        vertical_spacing=0.02, subplot_titles=("Row 1 title", "Row 2 title"))
+```
+
+Then the different [sr/df generic accessor](http://5.161.179.223:8000/static/js/vbt/api/generic/accessors/index.html#vectorbtpro.generic.accessors.GenericAccessor.areaplot) are added with ADD_TRACE_KWARGS and TRACE_KWARGS. Other types of plot available in [plotting module](http://5.161.179.223:8000/static/js/vbt/api/generic/plotting/index.html)
+
+```python
+#using accessor
+close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color="blue")))
+indvolume.vbt.barplot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))
+#using plotting module
+vbt.Bar(indvolume, fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))
+```
+
+* ADD_TRACE_KWARGS - determines positioning withing subplot
+```python
+add_trace_kwargs=dict(secondary_y=False,row=1, col=1)
+```
+* TRACE_KWARGS - other styling of trace
+```python
+trace_kwargs=dict(name="LONGS",
+                  line=dict(color="#ffe476"),
+                  marker=dict(color="limegreen"),
+                  fill=None,
+                  connectgaps=True)
+```
+
+## Example
+
+```python
+    fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, 
+                            specs=[[{"secondary_y": True}], [{"secondary_y": False}]], 
+                            vertical_spacing=0.02, subplot_titles=("Price and Indicators", "Volume"))
+
+    # Plotting the close price
+    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color="blue")))
+```
+
+# Data
+## Resampling
+```python
+t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample("1T")
+t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna()) #main session data only, no nans
+
+t5data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample("5T")
+t5data = t5data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
+
+dailydata = basic_data[['open', 'high', 'low', 'close', 'volume', 'vwap']].resample("D").dropna()
+
+#realign 5min close to 1min so it can be compared with 1min
+t5data_close_realigned = t5data.close.vbt.realign_closing("1T").between_time('09:30', '16:00').dropna() 
+#same with open
+t5data.open.vbt.realign_opening("1h")
+```
+### Define resample function for custom column
+Example of custom feature config [Binance Data](http://5.161.179.223:8000/static/js/vbt/api/data/custom/binance/index.html#vectorbtpro.data.custom.binance.BinanceData.feature_config).
+Other [reduced functions available](http://5.161.179.223:8000/static/js/vbt/api/generic/nb/apply_reduce/index.html). (mean, min, max, median, nth ...)
+```python
+from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig
+from vectorbtpro import _typing as tp
+from vectorbtpro.generic import nb as generic_nb
+
+_feature_config: tp.ClassVar[Config] = HybridConfig(
+    {
+        "buyvolume": dict(
+            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
+                resampler,
+                generic_nb.sum_reduce_nb,
+            )
+        ),
+        "sellvolume": dict(
+            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
+                resampler,
+                generic_nb.sum_reduce_nb,
+            )
+        )
+    }
+)
+
+basic_data._feature_config = _feature_config
+```
+
+### Validate resample
+```python
+t2dataclose = t2data.close.rename("15MIN - realigned").vbt.realign_closing("1T")
+fig = t1data.close.rename("1MIN").vbt.plot()
+t2data.close.rename("15MIN").vbt.plot(fig=fig)
+t2dataclose.vbt.plot(fig=fig)
+```
+## Persisting
+```python
+basic_data.to_parquet(partition_by="day", compression="gzip")  
+day_data = vbt.ParquetData.pull("BAC", filters=[("group", "==", "2024-05-03")]) 
+vbt.print_dir_tree("BTC-USD")#overeni directory structure
+```
+# Discover
+```python
+vbt.phelp(vbt.talib(“atr”).run) #parameters it accepts
+vbt.pdir(pf) - get available properties and methods
+vbt.pprint(basic_data) #to get correct shape, info about instance
+```