BLD: adjusting the marketplace sample algo

catalyst/examples/mean_reversion_by_marketcap.py

@@ -33,10 +33,14 @@ def initialize(context):
     # parameters or values you're going to use.
 
     # In our example, we're looking at Neo in Ether.
-    df = get_dataset(
-        'marketcap1234', start=context.datetime
-    )  # type: pd.DataFrame
+    df = get_dataset('testmarketcap2')  # type: pd.DataFrame
 
+    # Picking a specific date in our DataFrame
+    first_dt = df.index.get_level_values(0)[0]
+    # Since we use a MultiIndex with date / symbol, picking a date will
+    # result in a new DataFrame for the selected date with a single
+    # symbol index
+    df = df.xs(first_dt, level=0)
     # Keep only the top coins by market cap
     df = df.loc[df['market_cap_usd'].isin(df['market_cap_usd'].nlargest(100))]
 

catalyst/marketplace/marketplace.py

@@ -148,13 +148,13 @@ class Marketplace:
               'Gas Price:\t\t[Accept the default value]\n'
               'Nonce:\t\t\t{nonce}\n'
               'Data:\t\t\t{data}\n'.format(
-                _from=from_address,
-                to=tx['to'],
-                value=tx['value'],
-                gas=tx['gas'],
-                nonce=tx['nonce'],
-                data=tx['data'], )
-              )
+            _from=from_address,
+            to=tx['to'],
+            value=tx['value'],
+            gas=tx['gas'],
+            nonce=tx['nonce'],
+            data=tx['data'], )
+        )
 
         signed_tx = input('Copy and Paste the "Signed Transaction" '
                           'field here:\n')
@@ -247,9 +247,9 @@ class Marketplace:
         })
 
         try:
-            balance = Web3.toInt(balance)           # web3 >= 4.0.0b7
+            balance = Web3.toInt(balance)  # web3 >= 4.0.0b7
         except TypeError:
-            balance = Web3.toInt(hexstr=balance)    # web3 <= 4.0.0b6
+            balance = Web3.toInt(hexstr=balance)  # web3 <= 4.0.0b6
 
         if balance > grains:
             print('OK.')
@@ -259,14 +259,14 @@ class Marketplace:
                   'buy: {} ENG. Get enough ENG to cover the costs of the '
                   'monthly\nsubscription for what you are trying to buy, '
                   'and try again.'.format(
-                    address, from_grains(balance), price))
+                address, from_grains(balance), price))
             return
 
         while True:
             agree_pay = input('Please confirm that you agree to pay {} ENG '
                               'for a monthly subscription to the dataset "{}" '
                               'starting today. [default: Y] '.format(
-                                price, dataset)) or 'y'
+                price, dataset)) or 'y'
             if agree_pay.lower() not in ('y', 'n'):
                 print("Please answer Y or N.")
             else:
@@ -369,7 +369,7 @@ class Marketplace:
               'You can now ingest this dataset anytime during the '
               'next month by running the following command:\n'
               'catalyst marketplace ingest --dataset={}'.format(
-                dataset, address, dataset))
+            dataset, address, dataset))
 
     def process_temp_bundle(self, ds_name, path):
         """
@@ -426,10 +426,10 @@ class Marketplace:
             print('Your subscription to dataset "{}" expired on {} UTC.'
                   'Please renew your subscription by running:\n'
                   'catalyst marketplace subscribe --dataset={}'.format(
-                    ds_name,
-                    pd.to_datetime(check_sub[4], unit='s', utc=True),
-                    ds_name)
-                  )
+                ds_name,
+                pd.to_datetime(check_sub[4], unit='s', utc=True),
+                ds_name)
+            )
 
         if 'key' in self.addresses[address_i]:
             key = self.addresses[address_i]['key']
@@ -487,8 +487,9 @@ class Marketplace:
         df = z.todataframe()  # type: pd.DataFrame
         df.set_index(['date', 'symbol'], drop=True, inplace=True)
 
-        if start and end is None:
-            df = df.xs(start, level=0)
+        # TODO: implement the filter more carefully
+        # if start and end is None:
+        #     df = df.xs(start, level=0)
 
         return df