MOD: Apply various Python client library improvements

Author: cjdsellers

README.md

@@ -9,22 +9,22 @@
 The official Python client library for [Databento](https://databento.com).
 
 Key features include:
-- Fast, lightweight access to both live and historical data from [multiple markets]().
-- [Multiple schemas]() such as MBO, MBP, top of book, OHLCV, last sale, and more.
-- [Fully normalized](), i.e. identical message schemas for both live and historical data, across multiple asset classes.
-- Provides mappings between different symbology systems, including [smart symbology]() for futures rollovers.
+- Fast, lightweight access to both live and historical data from [multiple markets](https://docs0.databento.com/knowledge-base/new-users/venues-and-publishers?historical=python&live=python).
+- [Multiple schemas](https://docs0.databento.com/knowledge-base/new-users/list-of-supported-market-data-schemas?historical=python&live=python) such as MBO, MBP, top of book, OHLCV, last sale, and more.
+- [Fully normalized](https://docs0.databento.com/knowledge-base/new-users/normalization?historical=python&live=python), i.e. identical message schemas for both live and historical data, across multiple asset classes.
+- Provides mappings between different symbology systems, including [smart symbology](https://docs0.databento.com/reference-historical/basics/symbology?historical=python&live=python) for futures rollovers.
 - [Point-in-time]() instrument definitions, free of look-ahead bias and retroactive adjustments.
 - Reads and stores market data in an extremely efficient file format using [Databento Binary Encoding]().
-- Event-driven [market replay](), including at high-frequency order book granularity.
-- Support for [batch download]() of flat files.
-- Support for [pandas](), CSV, and JSON.
+- Event-driven [market replay](https://docs0.databento.com/reference-historical/helpers/bento-replay?historical=python&live=python), including at high-frequency order book granularity.
+- Support for [batch download](https://docs0.databento.com/knowledge-base/new-users/historical-data-streaming-vs-batch-download?historical=python&live=python) of flat files.
+- Support for [pandas](https://pandas.pydata.org/docs/), CSV, and JSON.
 
 ## Documentation
 The best place to begin is with our [Getting started](https://docs.databento.com/getting-started?historical=python&live=python) guide.
 
 You can find our full client API reference on the [Historical Reference](https://docs.databento.com/reference-historical?historical=python&live=python) and
 [Live Reference](https://docs.databento.com/reference-live?historical=python&live=python) sections of our documentation. See also the
-[Examples]() section for various tutorials and code samples.
+[Examples](https://docs0.databento.com/examples?historical=python&live=python) section for various tutorials and code samples.
 
 ## Requirements
 The library is fully compatible with the latest distribution of Anaconda 3.7 and above.
@@ -56,10 +56,11 @@ import databento as db
 client = db.Historical('YOUR_API_KEY')
 data = client.timeseries.stream(
     dataset='GLBX.MDP3',
-    start='2020-11-02T14:30',
-    end='2020-11-02T14:40')
+    start='2022-06-10T14:30',
+    end='2022-06-10T14:40',
+)
 
-data.replay(callback=print)    # market replay, with `print` as event handler
+data.replay(callback=print)  # market replay, with `print` as event handler
 ```
 
 Replace `YOUR_API_KEY` with an actual API key, then run this program.
@@ -70,7 +71,7 @@ and dispatch each data event to an event handler. You can also use
 
 ```python
 df = data.to_df(pretty_ts=True, pretty_px=True)  # to DataFrame, with pretty formatting
-array = data.to_ndarray()                        # to ndarray
+array = data.to_ndarray()  # to ndarray
 ```
 
 Note that the API key was also passed as a parameter, which is
@@ -81,7 +82,7 @@ Instead, you can leave out this parameter to pass your API key via the `DATABENT
 import databento as db
 
 client = db.Historical('YOUR_API_KEY')  # pass as parameter
-client = db.Historical()                # pass as `DATABENTO_API_KEY` environment variable
+client = db.Historical()  # pass as `DATABENTO_API_KEY` environment variable
 ```
 
 ## License

examples/historical_batch.py

@@ -11,10 +11,11 @@
 
     response = client.batch.submit_job(
         dataset="GLBX.MDP3",
-        symbols=["ESH1"],
+        symbols=["ESM2"],
         schema="mbo",
-        start="2020-12-27T12:00",
-        end="2020-12-29",
+        start="2022-06-10T12:00",
+        end="2022-06-10T14:00",
+        limit=1000,  # <-- limiting batch request to 1000 records only
         encoding="csv",
         compression="zstd",
         delivery="download",

examples/historical_metadata_get_billable_size.py

@@ -9,10 +9,10 @@
 
     size: int = client.metadata.get_billable_size(
         dataset="GLBX.MDP3",
-        symbols=["ESH1"],
+        symbols=["ESM2"],
         schema="mbo",
-        start="2020-12-28T12:00",
-        end="2020-12-29",
+        start="2022-06-10T12:00",
+        end="2022-06-10T14:00",
     )
 
     print(size)

examples/historical_metadata_get_cost.py

@@ -7,22 +7,12 @@
     key = "YOUR_API_KEY"
     client = db.Historical(key=key)
 
-    cost1: float = client.metadata.get_cost(
+    cost: float = client.metadata.get_cost(
         dataset="GLBX.MDP3",
-        symbols="*",
+        symbols="ESM2",
         schema="mbo",
-        start="2020-12-27T12:00",
-        end="2020-12-29",
+        start="2022-06-10",
+        end="2022-06-15",
     )
 
-    print(cost1)
-
-    cost2: float = client.metadata.get_cost(
-        dataset="XNAS.ITCH",
-        symbols=["MSFT"],
-        schema="trades",
-        start="2015-04-22",
-        end="2015-04-22T12:10",
-    )
-
-    print(cost2)
+    print(cost)

examples/historical_metadata_get_shape.py

@@ -11,10 +11,10 @@
 
     shape: Tuple = client.metadata.get_shape(
         dataset="GLBX.MDP3",
-        symbols=["ESH1"],
+        symbols=["ESM2"],
         schema="mbo",
-        start="2020-12-28T12:00",
-        end="2020-12-29",
+        start="2022-06-10T12:00",
+        end="2022-06-10T14:00",
         encoding="csv",
     )
 

examples/historical_symbology_resolve.py

@@ -12,11 +12,11 @@
 
     response = client.symbology.resolve(
         dataset="GLBX.MDP3",
-        symbols=["ESH1"],
+        symbols=["ESM2"],
         stype_in=SType.NATIVE,
         stype_out=SType.PRODUCT_ID,
-        start_date="2020-12-27",
-        end_date="2020-12-29",
+        start_date="2022-06-01",
+        end_date="2022-06-30",
     )
 
     pprint(response)

examples/historical_timeseries_async.py

@@ -13,10 +13,10 @@ async def request_stream_async():
 
     data: Bento = await client.timeseries.stream_async(
         dataset="GLBX.MDP3",
-        symbols=["ESH1"],
+        symbols=["ESM2"],
         schema="mbo",
-        start="2020-12-28T10:00",
-        end="2020-12-29T10:10",
+        start="2022-06-10T12:00",
+        end="2022-06-10T14:00",
         limit=1000,  # <-- limiting response to 1000 records only
     )
     pprint(data.to_df())

examples/historical_timeseries_disk_io.py

@@ -15,10 +15,10 @@
     # Execute request through client
     data: Bento = client.timeseries.stream(
         dataset="GLBX.MDP3",
-        symbols=["ESH1"],
+        symbols=["ESM2"],
         schema="mbo",
-        start="2020-12-27T12:00",
-        end="2020-12-29",
+        start="2022-06-10T12:00",
+        end="2022-06-10T14:00",
         limit=1000,  # <-- limiting response to 1000 records only
         path=path,
     )

examples/historical_timeseries_from_file.py

@@ -6,10 +6,10 @@
 
 if __name__ == "__main__":
     db.log = "debug"  # optional debug logging
+    ts_start = datetime.datetime.utcnow()
 
     # Can load from file path (if exists)
-    ts_start = datetime.datetime.utcnow()
-    data = Bento.from_file(path="test_data.mbo.dbz")
+    data = Bento.from_file(path="my_data.dbz")
 
     print(data.to_df())
     print(datetime.datetime.utcnow() - ts_start)

examples/historical_timeseries_replay.py

@@ -10,10 +10,10 @@
 
     data: Bento = client.timeseries.stream(
         dataset="GLBX.MDP3",
-        symbols=["ESH1"],
+        symbols=["ESM2"],
         schema="trades",
-        start="2020-12-27T10:00",
-        end="2020-12-28T23:10",
+        start="2022-06-10T12:00",
+        end="2022-06-10T14:00",
         limit=1000,  # <-- limiting response to 1000 records only
     )