cal = Calendars.calendar("USNYSE")
trades = db.t("LearnIris", "StockTrades").where("Date=`2017-08-25`")
trades = trades.where("cal.isBusinessTime(ExchangeTimestamp)")
# CATEGORY PLOTTING - SINGLE
t1c = db.t("LearnDeephaven", "StockTrades")\
.where("Date >`2017-08-20`", "USym = `MSFT`")\
.view("Date", "USym", "Last", "Size", "ExchangeTimestamp")
totalSharesByUSym = t1c.view("Date", "USym",
"SharesTraded=Size").sumBy("Date", "USym")
categoryPlot = Plot.catPlot("MSFT", totalSharesByUSym.where("USym = `MSFT`"), "Date", "SharesTraded")\
.chartTitle("Shares Traded")\
.show()
# CATEGORY PLOTTING - MULTIPLE
t2c = db.t("LearnDeephaven", "StockTrades")\
.where("Date >`2017-08-20`", "USym in `AAPL`, `MSFT`")\
.view("Date", "USym", "Last", "Size", "ExchangeTimestamp")
totalSharesByUSym2 = t2c.view("Date", "USym",
"SharesTraded=Size").sumBy("Date", "USym")
categoryPlot2 = Plot.catPlot("MSFT", totalSharesByUSym2.where("USym = `MSFT`"), "Date", "SharesTraded")\
.catPlot("AAPL", totalSharesByUSym2.where("USym = `AAPL`"), "Date", "SharesTraded")\
.chartTitle("Shares Traded")\
.show()
t = tt.emptyTable(50)\
.update("X = i + 5", "XLow = X -1", "XHigh = X + 1", "Y = Math.random() * 5", "YLow = Y - 1", "YHigh = Y + 1", "USym = i % 2 == 0 ? `AAPL` : `MSFT`")
p = plt.plot("S1", t, "X", "Y").lineColor("black").show()
p2 = plt.plot("S1", t, "X", "Y").plotStyle("bar").gradientVisible(True).show()
p3 = plt.plot(
"S1", t, "X",
"Y").plotStyle("scatter").pointColor("black").pointSize(2).show()
p4 = plt.plot("S1", t, "X", "Y").plotStyle("area").seriesColor("red").show()
p4 = plt.plot3d("S1", t, "X", "X", "Y").show()
pBy = plt.plotBy("S1", t, "X", "Y", "USym").show()
pBy = plt.plot3dBy("S1", t, "X", "X", "Y", "USym").show()
cp = plt.catPlot("S1", t, "X", "Y").lineColor("black").show()
cp2 = plt.catPlot("S1", t, "X",
"Y").plotStyle("bar").gradientVisible(True).show()
cp3 = plt.catPlot(
"S1", t, "X",
"Y").plotStyle("scatter").pointColor("black").pointSize(2).show()
cp4 = plt.catPlot("S1", t, "X",
"Y").plotStyle("area").seriesColor("red").show()
cp = plt.catPlot3d("S1", t, "X", "X", "Y").show()
cpBy = plt.catPlotBy("S1", t, "X", "Y", "USym").show()
cpBy = plt.catPlot3dBy("S1", t, "X", "X", "Y", "USym").show()
pp = plt.piePlot("S1", t, "X", "Y")
summaries = db.t("LearnDeephaven",
"EODTrades").where("ImportDate=`2017-11-01`")
# XY Series
timePlot = Plot.plot("Microsoft", trades.where("Sym=`MSFT`"), "ExchangeTimestamp", "Last")\
.show()
multiSeries = Plot.plot("Microsoft", trades.where("Sym=`MSFT`"), "ExchangeTimestamp", "Last")\
.twinX()\
.plot("Apple", trades.where("Sym=`AAPL`"), "ExchangeTimestamp", "Last")\
.chartTitle("Price Over Time")\
.show()
# Category
categoryPlot = Plot.catPlot("Shares Traded", totalShares, "Sym", "SharesTraded")\
.chartTitle("Total Shares")\
.show()
# Pie
pieChart = Plot.piePlot("Shares Traded", totalShares, "Sym", "SharesTraded")\
.chartTitle("Total Shares")\
.show()
# Histogram
histogram = Plot.histPlot("MSFT", trades.where("Sym=`MSFT`"), "Last", 3)\
.chartTitle("Price Intervals")\
.show()
# Category Histogram
catHist = Plot.catHistPlot("Number of Trades", trades, "Sym")\
.chartTitle("Trades per Symbol")\
