Merge pull request #17 from globophobe/fix/readme

Update readme

README.md

@@ -67,9 +67,6 @@ python proxy.py trades
 
 Then, configure a Cloud Workflow to collect data in the cloud. There is an example workflow in the [invoke tasks](https://github.com/globophobe/django-quant-candles/blob/main/demo/tasks.py).
 
-<img src="https://raw.githubusercontent.com/globophobe/django-quant-candles/main/docs/assets/
-diagrams_image.png" alt="Deployment example"/>
-
 Environment
 -----------
 

demo/streamlit/sampling_techniques.py

@@ -52,6 +52,8 @@ def get_data_frames() -> dict[Any, DataFrame]:
     return data_frames
 
 
+style = mpf.make_mpf_style(base_mpf_style="nightclouds")
+
 for candle, data_frame in get_data_frames().items():
     from quant_candles.constants import Frequency
     from quant_candles.models import AdaptiveCandle, TimeBasedCandle
@@ -64,14 +66,25 @@ def get_data_frames() -> dict[Any, DataFrame]:
         title = f"{total_minutes} minutes, {total_candles} candles"
     elif isinstance(candle, AdaptiveCandle):
         sample_type = candle.json_data["sample_type"].capitalize()
-        title = f"{sample_type}, {total_candles} candles"
+        target_candles_per_day = candle.json_data["target_candles_per_day"]
+        moving_average_number_of_days = candle.json_data[
+            "moving_average_number_of_days"
+        ]
+        title = (
+            f"{sample_type}, {total_candles} candles\n"
+            f"Target {target_candles_per_day} candles per day "
+            f"using {moving_average_number_of_days} day moving average"
+        )
+
     fig, ax = mpf.plot(
         data_frame,
-        title=title,
+        axtitle=title,
         type="candle",
+        datetime_format="%Y-%m-%d",
+        ylabel="",
+        style=style,
         show_nontrading=True,
         figsize=(15, 10),
-        style="nightclouds",
         returnfig=True,
     )
 

pyproject.toml

@@ -23,7 +23,6 @@ randomname = "*"
 [tool.poetry.dev-dependencies]
 black = "*"
 coverage = "*"
-diagrams = "*"
 django-filter = "*"
 django-semantic-admin = "*"
 django-storages = {version = "*", extras = ["google"]}

quant_candles/models/candles.py

@@ -184,7 +184,10 @@ def aggregate(
         raise NotImplementedError
 
     def get_data(
-        self, timestamp_from: datetime, timestamp_to: datetime, limit: int = 10000
+        self,
+        timestamp_from: datetime,
+        timestamp_to: datetime,
+        limit: Optional[int] = None,
     ) -> list:
         """Get data."""
         query = (
@@ -198,13 +201,15 @@ def get_data(
             .order_by("-timestamp")
             .values("timestamp", "json_data")
         )
-        candle_data = candle_data[:limit]
+        if limit:
+            candle_data = candle_data[:limit]
         candle_read_only_data = (
             CandleReadOnlyData.objects.filter(query)
             .order_by("-timestamp")
             .values("timestamp", "json_data")
         )
-        candle_read_only_data = candle_read_only_data[: limit - candle_data.count()]
+        if limit:
+            candle_read_only_data = candle_read_only_data[: limit - candle_data.count()]
         return list(chain(candle_data, candle_read_only_data))
 
     def get_trade_data_summary(

quant_candles/views/candles.py

@@ -30,8 +30,6 @@ def get(self, request: Request, *args, **kwargs) -> Response:
         params = serializer.validated_data
         candle = self.get_object()
         data = candle.get_data(
-            params["timestamp_from"],
-            params["timestamp_to"],
-            limit=params["limit"],
+            params["timestamp_from"], params["timestamp_to"], limit=params["limit"]
         )
         return Response(CandleDataSerializer(data, many=True).data)