diff --git a/04 Research Environment/06 Indicators/02 Data Point Indicators/04 Create Indicator Timeseries.html b/04 Research Environment/06 Indicators/02 Data Point Indicators/04 Create Indicator Timeseries.html
deleted file mode 100644
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+++ /dev/null
@@ -1,120 +0,0 @@
-You need to subscribe to some market data and create an indicator in order to calculate a timeseries of indicator values. In this example, use a 20-period 2-standard-deviation BollingerBands indicator.
-
-
var bb = new BollingerBands(20, 2);
-
bb = BollingerBands(20, 2)
-
You can create the indicator timeseries with the Indicator helper method or you can manually create the timeseries.
-
-Indicator Helper Method
-To create an indicator timeseries with the helper method, call the Indicator method.
-
-
// Create a dataframe with a date index, and columns are indicator values.
-var bbIndicator = qb.Indicator(bb, symbol, 50, Resolution.Daily);
-
# Create a dataframe with a date index, and columns are indicator values.
-bb_dataframe = qb.indicator(bb, symbol, 50, Resolution.DAILY)
-
-
-
-Manually Create the Indicator Timeseries
-Follow these steps to manually create the indicator timeseries:
-
-
- - Get some historical data.
-
-
// Request historical trading data with the daily resolution.
-var history = qb.History(symbol, 70, Resolution.Daily);
-
# Request historical trading data with the daily resolution.
-history = qb.history[TradeBar](symbol, 70, Resolution.DAILY)
-
- Create a
RollingWindow for each attribute of the indicator to hold their values.
-
-
// Create a window dictionary to store RollingWindow objects.
-var window = new Dictionary<string, RollingWindow<decimal>>();
-// Store the RollingWindow objects, index by key is the property of the indicator.
-var time = new RollingWindow<DateTime>(50);
-window["bollingerbands"] = new RollingWindow<decimal>(50);
-window["lowerband"] = new RollingWindow<decimal>(50);
-window["middleband"] = new RollingWindow<decimal>(50);
-window["upperband"] = new RollingWindow<decimal>(50);
-window["bandwidth"] = new RollingWindow<decimal>(50);
-window["percentb"] = new RollingWindow<decimal>(50);
-window["standarddeviation"] = new RollingWindow<decimal>(50);
-window["price"] = new RollingWindow<decimal>(50);
-
-
# Create a window dictionary to store RollingWindow objects.
-window = {}
-# Store the RollingWindow objects, index by key is the property of the indicator.
-window['time'] = RollingWindow[DateTime](50)
-window["bollingerbands"] = RollingWindow[float](50)
-window["lowerband"] = RollingWindow[float](50)
-window["middleband"] = RollingWindow[float](50)
-window["upperband"] = RollingWindow[float](50)
-window["bandwidth"] = RollingWindow[float](50)
-window["percentb"] = RollingWindow[float](50)
-window["standarddeviation"] = RollingWindow[float](50)
-window["price"] = RollingWindow[float](50)
-
-
- Attach a handler method to the indicator that updates the
RollingWindow objects.
-
-
// Define an update function to add the indicator values to the RollingWindow object.
-bb.Updated += (sender, updated) =>
-{
- var indicator = (BollingerBands)sender;
- time.Add(updated.EndTime);
- window["bollingerbands"].Add(updated);
- window["lowerband"].Add(indicator.LowerBand);
- window["middleband"].Add(indicator.MiddleBand);
- window["upperband"].Add(indicator.UpperBand);
- window["bandwidth"].Add(indicator.BandWidth);
- window["percentb"].Add(indicator.PercentB);
- window["standarddeviation"].Add(indicator.StandardDeviation);
- window["price"].Add(indicator.Price);
-};
-
# Define an update function to add the indicator values to the RollingWindow object.
-def update_bollinger_band_window(sender: object, updated: IndicatorDataPoint) -> None:
- indicator = sender
- window['time'].add(updated.end_time)
- window["bollingerbands"].add(updated.value)
- window["lowerband"].add(indicator.lower_band.current.value)
- window["middleband"].add(indicator.middle_band.current.value)
- window["upperband"].add(indicator.upper_band.current.value)
- window["bandwidth"].add(indicator.band_width.current.value)
- window["percentb"].add(indicator.percent_b.current.value)
- window["standarddeviation"].add(indicator.standard_deviation.current.value)
- window["price"].add(indicator.price.current.value)
-
-bb.updated += UpdateBollingerBandWindow
-
When the indicator receives new data, the preceding handler method adds the new IndicatorDataPoint values into the respective RollingWindow.
-
- - Iterate through the historical market data and update the indicator.
-
-
foreach(var bar in history)
-{
- bb.Update(bar.EndTime, bar.Close);
-}
-
for bar in history:
- bb.update(bar.end_time, bar.close)
-
- Display the data.
-
-
Console.WriteLine($"time,{string.Join(',', window.Select(kvp => kvp.Key))}");
-foreach (var i in Enumerable.Range(0, 5).Reverse())
-{
- var data = string.Join(", ", window.Select(kvp => Math.Round(kvp.Value[i],6)));
- Console.WriteLine($"{time[i]:yyyyMMdd}, {data}");
-}
-
-
- - Populate a
DataFrame with the data in the RollingWindow objects.
-
-
bb_dataframe = pd.DataFrame(window).set_index('time')
-
-
-
diff --git a/04 Research Environment/06 Indicators/02 Data Point Indicators/04 Create Indicator Timeseries.php b/04 Research Environment/06 Indicators/02 Data Point Indicators/04 Create Indicator Timeseries.php
new file mode 100644
index 0000000000..bd419e30ad
--- /dev/null
+++ b/04 Research Environment/06 Indicators/02 Data Point Indicators/04 Create Indicator Timeseries.php
@@ -0,0 +1,58 @@
+You need to subscribe to some market data and create an indicator in order to calculate a timeseries of indicator values. In this example, use a 20-period 2-standard-deviation BollingerBands indicator.
+
+
var bb = new BollingerBands(20, 2);
+
bb = BollingerBands(20, 2)
+
+
// Set the window.size to the desired timeseries length
+bb.Window.Size=50;
+bb.LowerBand.Window.Size=50;
+bb.MiddleBand.Window.Size=50;
+bb.UpperBand.Window.Size=50;
+bb.BandWidth.Window.Size=50;
+bb.PercentB.Window.Size=50;
+bb.StandardDeviation.Window.Size=50;
+bb.Price.Window.Size=50;
+
# Set the window.size to the desired timeseries length
+bb.window.size=50
+bb.lower_band.window.size=50
+bb.middle_band.window.size=50
+bb.upper_band.window.size=50
+bb.band_width.window.size=50
+bb.percent_b.window.size=50
+bb.standard_deviation.window.size=50
+bb.price.window.size=50
+
+
foreach (var i in Enumerable.Range(0, 5).Reverse())
+{
+ Console.WriteLine($"{bb[i].EndTime:yyyyMMdd} {bb[i].Value:f4} {bb.LowerBand[i].Value:f4} {bb.MiddleBand[i].Value:f4} {bb.UpperBand[i].Value:f4} {bb.BandWidth[i].Value:f4} {bb.PercentB[i].Value:f4} {bb.StandardDeviation[i].Value:f4} {bb.Price[i].Value:f4}");
+}
+
DataFrame with the data in the Indicator object.
+
+
bb_dataframe = pd.DataFrame({
+ "current": pd.Series({x.end_time: x.value for x in bb}),
+ "lower_band": pd.Series({x.end_time: x.value for x in bb.lower_band}),
+ "middle_band": pd.Series({x.end_time: x.value for x in bb.middle_band}),
+ "upper_band": pd.Series({x.end_time: x.value for x in bb.upper_band}),
+ "band_width": pd.Series({x.end_time: x.value for x in bb.band_width}),
+ "percent_b": pd.Series({x.end_time: x.value for x in bb.percent_b}),
+ "standard_deviation": pd.Series({x.end_time: x.value for x in bb.standard_deviation}),
+ "price": pd.Series({x.end_time: x.value for x in bb.price})
+}).sort_index()
+
You need to subscribe to some market data and create an indicator in order to calculate a timeseries of indicator values. In this example, use a 20-period AverageTrueRange indicator.
-
-
var atr = new AverageTrueRange(20);
-
atr = AverageTrueRange(20)
-
You can create the indicator timeseries with the Indicator helper method or you can manually create the timeseries.
-
-Indicator Helper Method
-To create an indicator timeseries with the helper method, call the Indicator method.
-
-
// Create a dataframe with a date index, and columns are ATR indicator values.
-var atrIndicator = qb.Indicator(atr, symbol, 50, Resolution.Daily);
-
# Create a dataframe with a date index, and columns are ATR indicator values.
-atr_dataframe = qb.indicator(atr, symbol, 50, Resolution.DAILY)
-
-
-
-
-Manually Create the Indicator Timeseries
-Follow these steps to manually create the indicator timeseries:
-
-
- - Get some historical data.
-
-
// Request historical trading data with the daily resolution.
-var history = qb.History(symbol, 70, Resolution.Daily);
-
# Request historical trading data with the daily resolution.
-history = qb.history[TradeBar](symbol, 70, Resolution.DAILY)
-
- Create a
RollingWindow for each attribute of the indicator to hold their values.
-
-
// Create a window dictionary to store RollingWindow objects.
-var window = new Dictionary<string, RollingWindow<decimal>>();
-// Store the RollingWindow objects, index by key is the property of the indicator.
-var time = new RollingWindow<DateTime>(50);
-window["averagetruerange"] = new RollingWindow<decimal>(50);
-window["truerange"] = new RollingWindow<decimal>(50);
-
-
# Create a window dictionary to store RollingWindow objects.
-window = {}
-# Store the RollingWindow objects, index by key is the property of the indicator.
-window['time'] = RollingWindow[DateTime](50)
-window['averagetruerange'] = RollingWindow[float](50)
-window["truerange"] = RollingWindow[float](50)
-
-
- Attach a handler method to the indicator that updates the
RollingWindow objects.
-
-
// Define an update function to add the indicator values to the RollingWindow object.
-atr.Updated += (sender, updated) =>
-{
- var indicator = (AverageTrueRange)sender;
- time.Add(updated.EndTime);
- window["averagetruerange"].Add(updated);
- window["truerange"].Add(indicator.TrueRange);
-};
-
# Define an update function to add the indicator values to the RollingWindow object.
-def update_average_true_range_window(sender: object, updated: IndicatorDataPoint) -> None:
- indicator = sender
- window['time'].add(updated.end_time)
- window["averagetruerange"].add(updated.value)
- window["truerange"].add(indicator.true_range.current.value)
-
-atr.updated += UpdateAverageTrueRangeWindow
-
When the indicator receives new data, the preceding handler method adds the new IndicatorDataPoint values into the respective RollingWindow.
-
- - Iterate through the historical market data and update the indicator.
-
-
foreach(var bar in history){
- // Update the indicators with the whole bar.
- atr.Update(bar);
-}
-
for bar in history:
- atr.update(bar)
-
- Display the data.
-
-
Console.WriteLine($"time,{string.Join(',', window.Select(kvp => kvp.Key))}");
-foreach (var i in Enumerable.Range(0, 5).Reverse())
-{
- var data = string.Join(", ", window.Select(kvp => Math.Round(kvp.Value[i],6)));
- Console.WriteLine($"{time[i]:yyyyMMdd}, {data}");
-}
-
-
- - Populate a
DataFrame with the data in the RollingWindow objects.
-
-
atr_dataframe = pd.DataFrame(window).set_index('time')
-
-
-
diff --git a/04 Research Environment/06 Indicators/03 Bar Indicators/04 Create Indicator Timeseries.php b/04 Research Environment/06 Indicators/03 Bar Indicators/04 Create Indicator Timeseries.php
new file mode 100644
index 0000000000..a74db7f1d9
--- /dev/null
+++ b/04 Research Environment/06 Indicators/03 Bar Indicators/04 Create Indicator Timeseries.php
@@ -0,0 +1,40 @@
+You need to subscribe to some market data and create an indicator in order to calculate a timeseries of indicator values. In this example, use a 20-period AverageTrueRange indicator.
+
+
var atr = new AverageTrueRange(20);
+
atr = AverageTrueRange(20)
+
+
// Set the window.size to the desired timeseries length
+atr.Window.Size = 50;
+atr.TrueRange.Window.Size = 50;
+
# Set the window.size to the desired timeseries length
+atr.window.size = 50
+atr.true_range.window.size = 50
+
+
foreach (var i in Enumerable.Range(0, 5).Reverse())
+{
+ Console.WriteLine($"{atr[i].EndTime:yyyyMMdd} {atr[i].Value:f4} {atr.TrueRange[i].Value:f4}");
+}
+
DataFrame with the data in the Indicator object.
+
+
atr_dataframe = pd.DataFrame({
+ "current": pd.Series({x.end_time: x.value for x in atr}),
+ "truerange": pd.Series({x.end_time: x.value for x in atr.true_range})
+}).sort_index()
+
You need to subscribe to some market data and create an indicator in order to calculate a timeseries of indicator values. In this example, use a 20-period VolumeWeightedAveragePriceIndicator indicator.
-
-
var vwap = new VolumeWeightedAveragePriceIndicator(20);
-
vwap = VolumeWeightedAveragePriceIndicator(20)
-
You can create the indicator timeseries with the Indicator helper method or you can manually create the timeseries.
-
-Indicator Helper Method
-To create an indicator timeseries with the helper method, call the Indicator method.
-
-
// Create a dataframe with a date index, and columns are indicator values.
-var vwapIndicator = qb.Indicator(vwap, symbol, 50, Resolution.Daily);
-
# Create a dataframe with a date index, and columns are indicator values.
-vwap_dataframe = qb.indicator(vwap, symbol, 50, Resolution.DAILY)
-
-
-
-Manually Create the Indicator Timeseries
-
-Follow these steps to create an indicator timeseries:
-
-
- - Get some historical data.
-
-
// Request historical trading data with the daily resolution.
-var history = qb.History(symbol, 70, Resolution.Daily);
-
# Request historical trading data with the daily resolution.
-history = qb.history[TradeBar](symbol, 70, Resolution.DAILY)
-
- Create a
RollingWindow for each attribute of the indicator to hold their values.
-
-
// Create a window dictionary to store RollingWindow objects.
-var window = new Dictionary<string, RollingWindow<decimal>>();
-// Store the RollingWindow objects, index by key is the property of the indicator.
-var time = new RollingWindow<DateTime>(50);
-window["volumeweightedaveragepriceindicator"] = new RollingWindow<decimal>(50);
-
-
# Create a window dictionary to store RollingWindow objects.
-window = {}
-# Store the RollingWindow objects, index by key is the property of the indicator.
-window['time'] = RollingWindow[DateTime](50)
-window['volumeweightedaveragepriceindicator'] = RollingWindow[float](50)
-
-
- Attach a handler method to the indicator that updates the
RollingWindow objects.
-
-
// Define an update function to add the indicator values to the RollingWindow object.
-vwap.Updated += (sender, updated) =>
-{
- time.Add(updated.EndTime);
- window["volumeweightedaveragepriceindicator"].Add(updated);
-};
-
# Define an update function to add the indicator values to the RollingWindow object.
-def update_vwap_window(sender: object, updated: IndicatorDataPoint) -> None:
- window['time'].add(updated.end_time)
- window["volumeweightedaveragepriceindicator"].add(updated.value)
-
-vwap.updated += UpdateVWAPWindow
-
When the indicator receives new data, the preceding handler method adds the new IndicatorDataPoint values into the respective RollingWindow.
-
-
- - Iterate through the historical market data and update the indicator.
-
-
foreach(var bar in history){
- // Update the indicators with the whole TradeBar.
- vwap.Update(bar);
-}
-
for bar in history:
- vwap.update(bar)
-
- Display the data.
-
-
Console.WriteLine($"time,{string.Join(',', window.Select(kvp => kvp.Key))}");
-foreach (var i in Enumerable.Range(0, 5).Reverse())
-{
- var data = string.Join(", ", window.Select(kvp => Math.Round(kvp.Value[i],6)));
- Console.WriteLine($"{time[i]:yyyyMMdd}, {data}");
-}
-
-
- - Populate a
DataFrame with the data in the RollingWindow objects.
-
-
vwap_dataframe = pd.DataFrame(window).set_index('time')
-
-
-
diff --git a/04 Research Environment/06 Indicators/04 Trade Bar Indicators/04 Create Indicator Timeseries.php b/04 Research Environment/06 Indicators/04 Trade Bar Indicators/04 Create Indicator Timeseries.php
new file mode 100644
index 0000000000..cc4e555263
--- /dev/null
+++ b/04 Research Environment/06 Indicators/04 Trade Bar Indicators/04 Create Indicator Timeseries.php
@@ -0,0 +1,37 @@
+You need to subscribe to some market data and create an indicator in order to calculate a timeseries of indicator values. In this example, use a 20-period VolumeWeightedAveragePriceIndicator indicator.
+
+
var vwap = new VolumeWeightedAveragePriceIndicator(20);
+
vwap = VolumeWeightedAveragePriceIndicator(20)
+
+
// Set the window.size to the desired timeseries length
+vwap.Window.Size = 50;
+
# Set the window.size to the desired timeseries length
+vwap.window.size = 50
+
+
foreach (var i in Enumerable.Range(0, 5).Reverse())
+{
+ Console.WriteLine($"{vwap[i].EndTime:yyyyMMdd} {vwap[i].Value:f4}");
+}
+
DataFrame with the data in the Indicator object.
+
+
vwap_dataframe = pd.DataFrame({
+ "current": pd.Series({x.end_time: x.value for x in vwap}))
+}).sort_index()
+
You can create the indicator timeseries with the Indicator helper method or you can manually create the timeseries.
+
+Indicator Helper Method
+To create an indicator timeseries with the helper method, call the Indicator method.
+
+
// Create a dataframe with a date index, and columns are indicator values.
+var Indicator = qb.Indicator(, symbol, 50, Resolution.Daily);
+
# Create a dataframe with a date index, and columns are indicator values.
+_dataframe = qb.indicator(, symbol, 50, Resolution.DAILY)
+
Manually Create the Indicator Timeseries
+Follow these steps to manually create the indicator timeseries:
+
+
+ - Get some historical data.
+
+
// Request historical trading data with the daily resolution.
+var history = qb.History(symbol, 70, Resolution.Daily);
+
# Request historical trading data with the daily resolution.
+history = qb.history[TradeBar](symbol, 70, Resolution.DAILY)
+
- Set the indicator
Window.Sizewindow.size for each attribute of the indicator to hold their values.
+
+
+ - Iterate through the historical market data and update the indicator.
+
+
foreach (var bar in history)
+{
+ .Update();
+}
+
for bar in history:
+ .update()
+
\ No newline at end of file