Add Quandl and Data Schema

AlphaPy now supports local data schema and Quandl feeds using the pandas Web data reader package.

.gitignore

@@ -24,3 +24,7 @@
 .idea/vcs.xml
 
 .idea/workspace.xml
+.idea/other.xml
+alphapy/examples/Trading System/.ipynb_checkpoints/A Trading System-checkpoint.ipynb
+*.pkl
+*.png

alphapy/data.py

@@ -31,7 +31,9 @@
 from alphapy.frame import read_frame
 from alphapy.globals import ModelType
 from alphapy.globals import Partition, datasets
-from alphapy.globals import PSEP, SSEP
+from alphapy.globals import PD_INTRADAY_OFFSETS
+from alphapy.globals import PD_WEB_DATA_FEEDS
+from alphapy.globals import PSEP, SSEP, USEP
 from alphapy.globals import SamplingMethod
 from alphapy.globals import WILDCARD
 
@@ -282,6 +284,53 @@ def sample_data(model):
     return model
 
 
+#
+# Function enhance_intraday_data
+#
+
+def enhance_intraday_data(df):
+    r"""Add columns to the intraday dataframe.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        The intraday dataframe.
+
+    Returns
+    -------
+    df : pandas.DataFrame
+        The dataframe with bar number and end-of-day columns.
+
+    """
+
+    # Convert the columns to proper data types
+
+    index_column = 'datetime'
+    dt_column = df['date'] + ' ' + df['time']
+    df[index_column] = pd.to_datetime(dt_column)
+    cols_float = ['open', 'high', 'low', 'close', 'volume']
+    df[cols_float] = df[cols_float].astype(float)
+
+    # Number the intraday bars
+
+    date_group = df.groupby('date')
+    df['bar_number'] = date_group.cumcount()
+
+    # Mark the end of the trading day
+
+    df['end_of_day'] = False
+    df.loc[date_group.tail(1).index, 'end_of_day'] = True
+
+    # Set the data frame's index
+    df.set_index(pd.DatetimeIndex(df[index_column]), drop=True, inplace=True)
+
+    # Return the enhanced frame
+
+    del df['date']
+    del df['time']
+    return df
+
+
 #
 # Function get_google_data
 #
@@ -345,25 +394,12 @@ def get_google_data(symbol, lookback_period, fractal):
             dt = datetime.fromtimestamp(day_item + (interval * offset))
             dt = pd.to_datetime(dt)
             dt_date = dt.strftime('%Y-%m-%d')
-            record = (dt, dt_date, open_item, high_item, low_item, close_item, volume_item)
+            dt_time = dt.strftime('%H:%M:%S')
+            record = (dt_date, dt_time, open_item, high_item, low_item, close_item, volume_item)
             records.append(record)
     # create data frame
-    cols = ['datetime', 'date', 'open', 'high', 'low', 'close', 'volume']
+    cols = ['date', 'time', 'open', 'high', 'low', 'close', 'volume']
     df = pd.DataFrame.from_records(records, columns=cols)
-    # convert to proper data types
-    cols_float = ['open', 'high', 'low', 'close']
-    df[cols_float] = df[cols_float].astype(float)
-    df['volume'] = df['volume'].astype(int)
-    # number the intraday bars
-    date_group = df.groupby('date')
-    df['bar_number'] = date_group.cumcount()
-    # mark the end of the trading day
-    df['end_of_day'] = False
-    del df['date']
-    df.loc[date_group.tail(1).index, 'end_of_day'] = True
-    # set the index to datetime
-    df.index = df['datetime']
-    del df['datetime']
     # return the dataframe
     return df
 
@@ -373,7 +409,7 @@ def get_google_data(symbol, lookback_period, fractal):
 #
 
 def get_pandas_data(schema, symbol, lookback_period):
-    r"""Get Yahoo Finance daily data.
+    r"""Get Pandas Web Reader data.
 
     Parameters
     ----------
@@ -391,32 +427,39 @@ def get_pandas_data(schema, symbol, lookback_period):
 
     """
 
+    # Quandl is a special case.
+
+    if 'quandl' in schema:
+        schema, symbol_prefix = schema.split(USEP)
+        symbol = SSEP.join([symbol_prefix, symbol]).upper()
+
     # Calculate the start and end date for Yahoo.
 
     start = datetime.now() - timedelta(lookback_period)
     end = datetime.now()
 
     # Call the Pandas Web data reader.
 
-    df = None
     try:
         df = web.DataReader(symbol, schema, start, end)
-        df = df.rename(columns = lambda x: x.lower().replace(' ',''))
     except:
+        df = None
         logger.info("Could not retrieve data for: %s", symbol)
 
     return df
 
 
 #
-# Function get_feed_data
+# Function get_market_data
 #
 
-def get_feed_data(group, lookback_period):
+def get_market_data(model, group, lookback_period, resample_data):
     r"""Get data from an external feed.
 
     Parameters
     ----------
+    model : alphapy.Model
+        The model object describing the data.
     group : alphapy.Group
         The group of symbols.
     lookback_period : int
@@ -429,27 +472,71 @@ def get_feed_data(group, lookback_period):
 
     """
 
+    # Unpack model specifications
+
+    directory = model.specs['directory']
+    extension = model.specs['extension']
+    separator = model.specs['separator']
+
+    # Unpack group elements
+
     gspace = group.space
     schema = gspace.schema
     fractal = gspace.fractal
+
     # Determine the feed source
-    if 'd' in fractal:
-        # daily data (date only)
-        logger.info("Getting Daily Data")
-        daily_data = True
-    else:
+
+    if any(substring in fractal for substring in PD_INTRADAY_OFFSETS):
         # intraday data (date and time)
-        logger.info("Getting Intraday Data (Google 50-day limit)")
-        daily_data = False
+        logger.info("Getting Intraday Data [%s] from %s", fractal, schema)
+        intraday_data = True
+        index_column = 'datetime'
+    else:
+        # daily data or higher (date only)
+        logger.info("Getting Daily Data [%s] from %s", fractal, schema)
+        intraday_data = False
+        index_column = 'date'
+
     # Get the data from the relevant feed
+
+    data_dir = SSEP.join([directory, 'data'])
+    pandas_data = any(substring in schema for substring in PD_WEB_DATA_FEEDS)
     n_periods = 0
+
     for item in group.members:
         logger.info("Getting %s data for last %d days", item, lookback_period)
-        if daily_data:
+        # Locate the data source
+        if schema == 'data':
+            fname = frame_name(item.lower(), gspace)
+            df = read_frame(data_dir, fname, extension, separator)
+            if not intraday_data:
+                df.set_index(pd.DatetimeIndex(df[index_column]),
+                             drop=True, inplace=True)
+        elif schema == 'google' and intraday_data:
+            df = get_google_data(item, lookback_period, fractal)
+        elif pandas_data:
             df = get_pandas_data(schema, item, lookback_period)
         else:
-            df = get_google_data(item, lookback_period, fractal)
+            logger.error("Unsupported Data Source: %s", schema)
+        # Now that we have content, standardize the data
         if df is not None and not df.empty:
+            logger.info("Rows: %d", len(df))
+            # standardize column names
+            df = df.rename(columns = lambda x: x.lower().replace(' ',''))
+            # add intraday columns if necessary
+            if intraday_data:
+                df = enhance_intraday_data(df)
+            # order by increasing date if necessary
+            df = df.sort_index()
+            # resample data
+            if resample_data:
+                df = df.resample(fractal).agg({'open'   : 'first',
+                                               'high'   : 'max',
+                                               'low'    : 'min',
+                                               'close'  : 'last',
+                                               'volume' : 'sum'})
+                logger.info("Rows after Resampling at %s: %d",
+                            fractal, len(df))
             # allocate global Frame
             newf = Frame(item.lower(), gspace, df)
             if newf is None:
@@ -460,5 +547,6 @@ def get_feed_data(group, lookback_period):
                 n_periods = df_len
         else:
             logger.info("No DataFrame for %s", item)
+
     # The number of periods actually retrieved
     return n_periods