Added tags to python algorithms

Algorithm.CSharp/CustomChartingAlgorithm.cs

@@ -16,7 +16,7 @@
 using System;
 using QuantConnect.Data.Market;
 
-namespace QuantConnect.Algorithm.Examples
+namespace QuantConnect.Algorithm.CSharp
 {
     /// <summary>
     /// Algorithm demonstrating custom charting support in QuantConnect.

Algorithm.CSharp/DataConsolidationAlgorithm.cs

@@ -33,6 +33,8 @@ namespace QuantConnect.Algorithm.CSharp
     /// When a new 'consolidated' piece of data is produced by the IDataConsolidator, an event is fired
     /// with the argument of the new data.
     /// </summary>
+    /// <meta name="tag" content="using data" />
+    /// <meta name="tag" content="consolidating data" />
     public class DataConsolidationAlgorithm : QCAlgorithm
     {
         private TradeBar _last;

Algorithm.Python/CustomBenchmarkAlgorithm.py

@@ -1,10 +1,10 @@
 # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
 # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License"); 
+#
+# 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.
@@ -20,19 +20,22 @@
 from QuantConnect import *
 from QuantConnect.Algorithm import *
 
-
+### <summary>
+### Shows how to set a custom benchmark for you algorithms
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="benchmarks" />
 class CustomBenchmarkAlgorithm(QCAlgorithm):
-    '''Basic template algorithm simply initializes the date range and cash'''
 
     def Initialize(self):
         '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
-        
+
         self.SetStartDate(2013,10,07)  #Set Start Date
         self.SetEndDate(2013,10,11)    #Set End Date
         self.SetCash(100000)           #Set Strategy Cash
         # Find more symbols here: http://quantconnect.com/data
         self.AddEquity("SPY", Resolution.Second)
-        
+
         self.SetBenchmark("SPY");
 
     def OnData(self, data):

Algorithm.Python/CustomChartingAlgorithm.py

@@ -1,10 +1,10 @@
 # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
 # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License"); 
+#
+# 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.
@@ -28,14 +28,23 @@
 import decimal as d
 from datetime import timedelta, datetime
 
-
+### <summary>
+### Algorithm demonstrating custom charting support in QuantConnect.
+### The entire charting system of quantconnect is adaptable. You can adjust it to draw whatever you'd like.
+### Charts can be stacked, or overlayed on each other. Series can be candles, lines or scatter plots.
+### Even the default behaviours of QuantConnect can be overridden.
+### </summary>
+### <meta name="tag" content="charting" />
+### <meta name="tag" content="adding charts" />
+### <meta name="tag" content="series types" />
+### <meta name="tag" content="plotting indicators" />
 class CustomChartingAlgorithm(QCAlgorithm):
     def Initialize(self):
         self.SetStartDate(2016,1,1)
         self.SetEndDate(2017,1,1)
         self.SetCash(100000)
         self.AddEquity("SPY", Resolution.Daily)
-        
+
         # In your initialize method:
 		# Chart - Master Container for the Chart:
         stockPlot = Chart("Trade Plot")
@@ -44,7 +53,7 @@ def Initialize(self):
         stockPlot.AddSeries(Series("Sell", SeriesType.Scatter, 0))
         stockPlot.AddSeries(Series("Price", SeriesType.Line, 0))
         self.AddChart(stockPlot)
-        
+
         avgCross = Chart("Strategy Equity")
         avgCross.AddSeries(Series("FastMA", SeriesType.Line, 1))
         avgCross.AddSeries(Series("SlowMA", SeriesType.Line, 1))
@@ -54,7 +63,7 @@ def Initialize(self):
         self.slowMA = 0
         self.resample = datetime.min
         self.resamplePeriod = (self.EndDate - self.StartDate) / 2000
-        
+
     def OnData(self, slice):
         if slice["SPY"] is None:
             self.lastPrice = 0
@@ -70,7 +79,7 @@ def OnData(self, slice):
             self.resample = self.Time  + self.resamplePeriod
             self.Plot("Strategy Equity", "FastMA", self.fastMA);
             self.Plot("Strategy Equity", "SlowMA", self.slowMA);
-            
+
         # On the 5th days when not invested buy:
         if not self.Portfolio.Invested and self.Time.day % 13 == 0:
         	self.Order("SPY", (int)(self.Portfolio.MarginRemaining / self.lastPrice))

Algorithm.Python/CustomDataBitcoinAlgorithm.py

@@ -1,10 +1,10 @@
 # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
 # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License"); 
+#
+# 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.
@@ -27,12 +27,15 @@
 import numpy as np
 import json
 
-
+### <summary>
+### Demonstration of using an external custom datasource. LEAN Engine is incredibly flexible and allows you to define your own data source.
+### This includes any data source which has a TIME and VALUE. These are the *only* requirements. To demonstrate this we're loading in "Bitcoin" data.
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="custom data" />
+### <meta name="tag" content="importing data" />
+### <meta name="tag" content="crypto" />
 class CustomDataBitcoinAlgorithm(QCAlgorithm):
-    '''3.0 CUSTOM DATA SOURCE: USE YOUR OWN MARKET DATA (OPTIONS, FOREX, FUTURES, DERIVATIVES etc).
-    The new QuantConnect Lean Backtesting Engine is incredibly flexible and allows you to define your own data source. 
-    This includes any data source which has a TIME and VALUE. These are the *only* requirements.
-    To demonstrate this we're loading in "Bitcoin" data.'''
 
     def Initialize(self):
         self.SetStartDate(2011, 9, 13)
@@ -41,29 +44,29 @@ def Initialize(self):
 
         # Define the symbol and "type" of our generic data:
         self.AddData(Bitcoin, "BTC")
-        
+
 
     def OnData(self, data):
         if "BTC" not in data: return
 
         close = data["BTC"].Close
-        
+
         # If we don't have any weather "SHARES" -- invest"
         if not self.Portfolio.Invested:
-            # Weather used as a tradable asset, like stocks, futures etc. 
+            # Weather used as a tradable asset, like stocks, futures etc.
             self.SetHoldings("BTC", 1)
             self.Debug("Buying BTC 'Shares': BTC: {0}".format(close))
-        
+
         self.Debug("Time: {0} {1}".format(datetime.now(), close))
 
 
 class Bitcoin(PythonData):
     '''Custom Data Type: Bitcoin data from Quandl - http://www.quandl.com/help/api-for-bitcoin-data'''
- 
+
     def GetSource(self, config, date, isLiveMode):
         if isLiveMode:
             return SubscriptionDataSource("https://www.bitstamp.net/api/ticker/", SubscriptionTransportMedium.Rest);
-            
+
         #return "http://my-ftp-server.com/futures-data-" + date.ToString("Ymd") + ".zip";
         # OR simply return a fixed small data file. Large files will slow down your backtest
         return SubscriptionDataSource("http://www.quandl.com/api/v1/datasets/BCHARTS/BITSTAMPUSD.csv?sort_order=asc", SubscriptionTransportMedium.RemoteFile);
@@ -72,19 +75,19 @@ def GetSource(self, config, date, isLiveMode):
     def Reader(self, config, line, date, isLiveMode):
         coin = Bitcoin()
         coin.Symbol = config.Symbol
-            
+
         if isLiveMode:
             # Example Line Format:
             # {"high": "441.00", "last": "421.86", "timestamp": "1411606877", "bid": "421.96", "vwap": "428.58", "volume": "14120.40683975", "low": "418.83", "ask": "421.99"}
             try:
                 liveBTC = json.loads(line)
-                
+
                 # If value is zero, return None
                 value = decimal.Decimal(liveBTC["last"])
                 if value == 0: return None
 
                 coin.Time = datetime.now()
-                coin.Value = value                
+                coin.Value = value
                 coin["Open"] = float(liveBTC["open"])
                 coin["High"] = float(liveBTC["high"])
                 coin["Low"] = float(liveBTC["low"])
@@ -102,10 +105,10 @@ def Reader(self, config, line, date, isLiveMode):
         # Date      Open   High    Low     Close   Volume (BTC)    Volume (Currency)   Weighted Price
         # 2011-09-13 5.8    6.0     5.65    5.97    58.37138238,    346.0973893944      5.929230648356
         if not (line.strip() and line[0].isdigit()): return None
-        
+
         try:
             data = line.split(',')
-            
+
             # If value is zero, return None
             value = decimal.Decimal(data[4])
             if value == 0: return None
@@ -120,7 +123,7 @@ def Reader(self, config, line, date, isLiveMode):
             coin["VolumeUSD"] = float(data[6])
             coin["WeightedPrice"] = float(data[7])
             return coin;
-            
+
         except ValueError:
             # Do nothing, possible error in json decoding
             return None

Algorithm.Python/CustomDataNIFTYAlgorithm.py

@@ -1,10 +1,10 @@
 # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
 # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License"); 
+#
+# 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.
@@ -27,12 +27,15 @@
 import math
 import json
 
+### <summary>
+### This demonstration imports indian NSE index "NIFTY" as a tradable security in addition to the USDINR currency pair. We move into the
+### NSE market when the economy is performing well.
+### </summary>
+### <meta name="tag" content="strategy examples" />
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="importing data" />
+### <meta name="tag" content="custom data" />
 class CustomDataNIFTYAlgorithm(QCAlgorithm):
-    '''3.0 CUSTOM DATA SOURCE: USE YOUR OWN MARKET DATA (OPTIONS, FOREX, FUTURES, DERIVATIVES etc).
-    The new QuantConnect Lean Backtesting Engine is incredibly flexible and allows you to define your own data source. 
-    This includes any data source which has a TIME and VALUE. These are the *only* requirements. 
-    To demonstrate this we're loading in "Nifty" data. This by itself isn't special, the cool part is next:
-    We load the "Nifty" data as a tradable security we're calling "NIFTY".'''
 
     def Initialize(self):
         self.SetStartDate(2008, 1, 8)
@@ -42,11 +45,11 @@ def Initialize(self):
         # Define the symbol and "type" of our generic data:
         self.AddData(DollarRupee, "USDINR")
         self.AddData(Nifty, "NIFTY")
-        
+
         self.minimumCorrelationHistory = 50
         self.today = CorrelationPair()
         self.prices = []
-        
+
 
     def OnData(self, data):
         if "USDINR" in data:
@@ -61,7 +64,7 @@ def OnData(self, data):
             self.prices.append(self.today)
             if len(self.prices) > self.minimumCorrelationHistory:
                 self.prices.pop(0)
-        
+
         # Strategy
         if self.Time.weekday() != 2: return
 
@@ -82,28 +85,28 @@ class Nifty(PythonData):
     '''NIFTY Custom Data Class'''
     def GetSource(self, config, date, isLiveMode):
         return SubscriptionDataSource("https://www.dropbox.com/s/rsmg44jr6wexn2h/CNXNIFTY.csv?dl=1", SubscriptionTransportMedium.RemoteFile);
-        
+
 
     def Reader(self, config, line, date, isLiveMode):
         if not (line.strip() and line[0].isdigit()): return None
 
         # New Nifty object
         index = Nifty();
         index.Symbol = config.Symbol
-        
+
         try:
             # Example File Format:
             # Date,       Open       High        Low       Close     Volume      Turnover
             # 2011-09-13  7792.9    7799.9     7722.65    7748.7    116534670    6107.78
             data = line.split(',')
-            index.Time = datetime.strptime(data[0], "%Y-%m-%d")            
+            index.Time = datetime.strptime(data[0], "%Y-%m-%d")
             index.Value = decimal.Decimal(data[4])
             index["Open"] = float(data[1])
             index["High"] = float(data[2])
             index["Low"] = float(data[3])
             index["Close"] = float(data[4])
-                
-        
+
+
         except ValueError:
                 # Do nothing
                 return None
@@ -122,13 +125,13 @@ def Reader(self, config, line, date, isLiveMode):
         # New USDINR object
         currency = DollarRupee();
         currency.Symbol = config.Symbol
-        
+
         try:
             data = line.split(',')
             currency.Time = datetime.strptime(data[0], "%Y-%m-%d")
             currency.Value = decimal.Decimal(data[1])
             currency["Close"] = float(data[1])
-            
+
         except ValueError:
             # Do nothing
             return None

Algorithm.Python/CustomDataRegressionAlgorithm.py

@@ -1,10 +1,10 @@
 # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
 # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License"); 
+#
+# 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.
@@ -24,17 +24,24 @@
 from QuantConnect.Data.UniverseSelection import *
 from datetime import datetime
 
-
+### <summary>
+### Regression test to demonstrate importing and trading on custom data.
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="importing data" />
+### <meta name="tag" content="custom data" />
+### <meta name="tag" content="crypto" />
+### <meta name="tag" content="regression test" />
 class CustomDataRegressionAlgorithm(QCAlgorithm):
-    
+
     ''' Regression algorithm for custom data '''
 
     def Initialize(self):
-        
+
         self.SetStartDate(2014,04,01)  #Set Start Date
         self.SetEndDate(2015,04,30)    #Set End Date
         self.SetCash(50000)            #Set Strategy Cash
-        
+
         self.AddData[Bitcoin]("BTC", Resolution.Daily)
 
     def OnData(self, data):