Added more QuickChart types + examples

README.md

@@ -114,9 +114,11 @@ In finmarketpy/examples you will find several examples, including some simple tr
 
 # finmarketpy log
 
+* 07 May 2020
+    * Improved QuickChart, adding additional labels, fixing example
 * 06 May 2020
     * Added QuickChart for one line download of market data and plotting
-    * Added feature to resample returns 
+    * Added feature to resample returns
     * Allow more custom parameters in backtest
 * 17 Dec 2019
     * Added link for Python for finance workshop installation notes for Anaconda

finmarketpy/economics/quickchart.py

@@ -15,13 +15,17 @@
 from chartpy import Chart, Style, ChartConstants
 from findatapy.util.dataconstants import DataConstants
 from findatapy.market import Market, MarketDataRequest, MarketDataGenerator
+from findatapy.timeseries import Calculations
 
 import datetime
 
 from datetime import timedelta
 
+dataconstants = DataConstants()
+
 class QuickChart(object):
-    """Displays charts from downloaded data, in a single line code call. Ideal for quickly generating charts
+    """Displays charts from downloaded data, in a single line code call. Ideal for quickly generating charts from sources
+    including Bloomberg, Quandl, ALFRED/FRED etc.
 
     """
 
@@ -30,8 +34,13 @@ def __init__(self, engine='plotly', data_source='bloomberg', market_data_generat
         self._market = Market(market_data_generator=market_data_generator)
         self._data_source = data_source
 
-    def plot_chart(self, tickers=None, tickers_rhs=None, start_date=None, finish_date=None, chart_file=None, chart_type='line', title='',
-                   fields={'close' : 'PX_LAST'}, freq='daily'):
+    def plot_chart(self, tickers=None, tickers_rhs=None, start_date=None, finish_date=None,
+                   chart_file=None, chart_type='line', title='',
+                   fields={'close' : 'PX_LAST'}, freq='daily', source='Web', brand_label='Cuemacro', display_brand_label=True,
+                   reindex=False, yoy=False, plotly_plot_mode='offline_png',
+                   quandl_api_key=dataconstants.quandl_api_key,
+                   fred_api_key=dataconstants.fred_api_key,
+                   alpha_vantage_api_key=dataconstants.alpha_vantage_api_key):
 
         if start_date is None:
             start_date = datetime.datetime.utcnow().date() - timedelta(days=60)
@@ -66,21 +75,41 @@ def plot_chart(self, tickers=None, tickers_rhs=None, start_date=None, finish_dat
         md_request = MarketDataRequest(start_date=start_date, finish_date=finish_date,
                                        freq=freq,
                                        data_source=self._data_source,
-                                       tickers=list(tickers.keys()), vendor_tickers=list(tickers.keys()),
+                                       tickers=list(tickers.keys()), vendor_tickers=list(tickers.values()),
                                        fields=list(fields.keys()),
-                                       vendor_fields=list(fields.values()))
+                                       vendor_fields=list(fields.values()),
+                                       quandl_api_key=quandl_api_key,
+                                       fred_api_key=fred_api_key,
+                                       alpha_vantage_api_key=alpha_vantage_api_key)
 
         df = self._market.fetch_market(md_request=md_request)
         df = df.fillna(method='ffill')
         df.columns = [x.split('.')[0] for x in df.columns]
 
         style = Style(title=title, chart_type=chart_type, html_file_output=chart_file, scale_factor=-1,
                       height=400, width=600, file_output=datetime.date.today().strftime("%Y%m%d") + " " + title + ".png",
-                      plotly_plot_mode='offline_png')
+                      plotly_plot_mode=plotly_plot_mode, source=source, brand_label=brand_label,
+                      display_brand_label=display_brand_label)
+
+        if reindex:
+            df = Calculations().create_mult_index_from_prices(df)
+
+            style.y_title = 'Reindexed from 100'
+
+        if yoy:
+            if freq == 'daily':
+                obs_in_year = 252
+            elif freq == 'intraday':
+                obs_in_year = 1440
+
+            df_rets = Calculations().calculate_returns(df)
+            df = Calculations().average_by_annualised_year(df_rets, obs_in_year=obs_in_year) * 100
+
+            style.y_title = 'Annualized % YoY'
 
         if list(tickers_rhs.keys()) != []:
             style.y_axis_2_series=list(tickers_rhs.keys())
             style.y_axis_2_showgrid = False
             style.y_axis_showgrid = False
 
-        return self._chart.plot(df, style=style)
+        return self._chart.plot(df, style=style), df

finmarketpy_examples/quandl_examples.py

@@ -32,7 +32,7 @@
 market = Market(market_data_generator=MarketDataGenerator())
 
 # Choose run_example = 0 for everything
-# run_example = 1 - download BoE data from quandl
+# run_example = 1 - download
 
 run_example = 0
 
@@ -60,4 +60,4 @@
     style.file_output = "boe-rate.png"
     style.source = 'Quandl/BoE'
 
-    chart.plot(df, style=style)
+    chart.plot(df, style=style)

finmarketpy_examples/quickchart_examples.py

@@ -0,0 +1,52 @@
+__author__ = 'saeedamen'
+
+#
+# Copyright 2020 Cuemacro
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
+# License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#
+# See the License for the specific language governing permissions and limitations under the License.
+#
+
+"""
+Shows how to use QuickChart to quickly download data and plot it
+"""
+
+# Choose run_example = 0 for everything
+# run_example = 1 - Plot S&P 500 charts with Matplotlib
+# run_example = 1 - Plot FX chart with Plotly
+
+run_example = 0
+
+###### Plot with Matplotlib
+if run_example == 1 or run_example == 0:
+    from finmarketpy.economics import QuickChart
+
+    # Plot with Matplotlib - S&P 500 on LHS y-axis and S&P 500 net long spec positioning on RHS y-axis
+    QuickChart(engine='matplotlib', data_source='bloomberg').plot_chart(tickers={'S&P500' : 'SPX Index'},
+        tickers_rhs={'Net long spec S&P 500 futures' : 'IMM0ENCN Index'},
+        title='S&P500 vs. net spec pos RHS (2008-2010)',
+                start_date='01 Jan 2007', finish_date='01 Jan 2010', source='Bloomberg')
+
+    # Plot with Matplotlib - S&P 500 YoY returns
+    QuickChart(engine='matplotlib', data_source='bloomberg').plot_chart(tickers={'S&P500' : 'SPX Index'},
+                title='S&P 500 YoY', chart_type='bar',
+                start_date='01 Jan 2007', yoy=True, source='Bloomberg')
+
+###### Plot with Plotly and Matplotlib
+if run_example == 2 or run_example == 0:
+    from finmarketpy.economics import QuickChart
+
+    # Plot with matplotlib - Major USD crosses reindexed from 100 in 2020
+    QuickChart(engine='matplotlib', data_source='bloomberg').plot_chart(tickers=['EURUSD Curncy', 'GBPUSD Curncy', 'AUDUSD Curncy'],
+    title='USD crosses in 2020',
+            start_date='01 Jan 2020', reindex=True, source='Bloomberg')
+
+    # Plot with Plotly - Major USD crosses reindexed from 100 in 2020
+    QuickChart(engine='plotly', data_source='bloomberg').plot_chart(tickers=['EURUSD Curncy', 'GBPUSD Curncy', 'AUDUSD Curncy'],
+    title='USD crosses in 2020 (Plotly)',
+            start_date='01 Jan 2020', reindex=True, source='Bloomberg')