/// <summary>
/// Load the market data.
/// </summary>
/// <returns> True if the data was loaded. </returns>
private bool LoadMarketData()
{
try
{
IMarketLoader loader = new YahooFinanceLoader();
var ticker = new TickerSymbol(Company.Text);
IList <MarketDataType> needed = new List <MarketDataType>();
needed.Add(MarketDataType.AdjustedClose);
needed.Add(MarketDataType.Close);
needed.Add(MarketDataType.Open);
needed.Add(MarketDataType.High);
needed.Add(MarketDataType.Low);
DateTime from = starting - TimeSpan.FromDays(365);
DateTime to = starting + TimeSpan.FromDays(365 * 2);
marketData = (List <LoadedMarketData>)loader.Load(ticker, needed, from, to);
marketData.Sort();
numberOfDays = (int)((ActualWidth - FirstDayOffset) / DayWidth);
numberOfDays = Math.Min(numberOfDays, marketData.Count);
return(true);
}
catch (Exception e)
{
MessageBox.Show("Ticker symbol likely invalid.\n" + e.Message, "Error Loading Data");
return(false);
}
}
public static void Generate(FileInfo dataDir)
{
IMarketLoader loader = new YahooFinanceLoader();
var market = new MarketMLDataSet(loader,
Config.INPUT_WINDOW, Config.PREDICT_WINDOW);
var desc = new MarketDataDescription(
Config.TICKER, MarketDataType.AdjustedClose, true, true);
market.AddDescription(desc);
var end = DateTime.Now; // end today
var begin = new DateTime(end.Ticks); // begin 30 days ago
// Gather training data for the last 2 years, stopping 60 days short of today.
// The 60 days will be used to evaluate prediction.
begin = begin.AddDays(-60);
end = end.AddDays(-60);
begin = begin.AddYears(-2);
market.Load(begin, end);
market.Generate();
EncogUtility.SaveEGB(FileUtil.CombinePath(dataDir, Config.TRAINING_FILE), market);
// create a network
BasicNetwork network = EncogUtility.SimpleFeedForward(
market.InputSize,
Config.HIDDEN1_COUNT,
Config.HIDDEN2_COUNT,
market.IdealSize,
true);
// save the network and the training
EncogDirectoryPersistence.SaveObject(FileUtil.CombinePath(dataDir, Config.NETWORK_FILE), network);
}
/// <summary>
/// Called to load training data for a company. This is how the training data is actually created.
/// To prepare input data for recognition use the CreateData method. The training set will be
/// added to. This allows the network to learn from multiple companies if this method is called
/// multiple times.
/// </summary>
/// <param name="symbol">The ticker symbol.</param>
/// <param name="training">The training set to add to.</param>
/// <param name="from">Beginning date</param>
/// <param name="to">Ending date</param>
public void LoadCompany(String symbol, BasicMLDataSet training, DateTime from, DateTime to)
{
IMarketLoader loader = new YahooFinanceLoader();
var ticker = new TickerSymbol(symbol);
IList <MarketDataType> dataNeeded = new List <MarketDataType>();
dataNeeded.Add(MarketDataType.AdjustedClose);
dataNeeded.Add(MarketDataType.Close);
dataNeeded.Add(MarketDataType.Open);
dataNeeded.Add(MarketDataType.High);
dataNeeded.Add(MarketDataType.Low);
var results = (List <LoadedMarketData>)loader.Load(ticker, dataNeeded, from, to);
results.Sort();
for (var index = PredictWindow; index < results.Count - EvalWindow; index++)
{
var data = results[index];
// determine bull or bear position, or neither
var bullish = false;
var bearish = false;
for (int search = 1; search <= EvalWindow; search++)
{
var data2 = results[index + search];
var priceBase = data.GetData(MarketDataType.AdjustedClose);
var priceCompare = data2.GetData(MarketDataType.AdjustedClose);
var diff = priceCompare - priceBase;
var percent = diff / priceBase;
if (percent > BullPercent)
{
bullish = true;
}
else if (percent < BearPercent)
{
bearish = true;
}
}
IMLDataPair pair = null;
if (bullish)
{
pair = CreateData(results, index, true);
}
else if (bearish)
{
pair = CreateData(results, index, false);
}
if (pair != null)
{
training.Add(pair);
}
}
}
public void Loader()
{
IMarketLoader loader = new YahooFinanceLoader();
var from = new DateTime(2008, 8, 4);
var to = new DateTime(2008, 8, 5);
ICollection <LoadedMarketData> list = loader.Load(new TickerSymbol("aapl"), null, from, to);
IEnumerator <LoadedMarketData> itr = list.GetEnumerator();
itr.MoveNext();
LoadedMarketData data = itr.Current;
Assert.AreEqual(160, (int)data.GetData(MarketDataType.Close));
itr.MoveNext();
data = itr.Current;
Assert.AreEqual(153, (int)data.GetData(MarketDataType.Close));
}
public static MarketMLDataSet GrabData()
{
IMarketLoader loader = new YahooFinanceLoader();
var result = new MarketMLDataSet(loader,
Config.INPUT_WINDOW, Config.PREDICT_WINDOW);
var desc = new MarketDataDescription(Config.TICKER,
MarketDataType.AdjustedClose, true, true);
result.AddDescription(desc);
var end = DateTime.Now; // end today
var begin = new DateTime(end.Ticks); // begin 30 days ago
begin = begin.AddDays(-60);
result.Load(begin, end);
result.Generate();
return(result);
}
public void MarketData()
{
IMarketLoader loader = new YahooFinanceLoader();
var tickerAAPL = new TickerSymbol("AAPL", null);
var tickerMSFT = new TickerSymbol("MSFT", null);
var marketData = new MarketMLDataSet(loader, 5, 1);
marketData.AddDescription(new MarketDataDescription(tickerAAPL, MarketDataType.Close, true, true));
marketData.AddDescription(new MarketDataDescription(tickerMSFT, MarketDataType.Close, true, false));
marketData.AddDescription(new MarketDataDescription(tickerAAPL, MarketDataType.Volume, true, false));
marketData.AddDescription(new MarketDataDescription(tickerMSFT, MarketDataType.Volume, true, false));
var begin = new DateTime(2008, 7, 1);
var end = new DateTime(2008, 7, 31);
marketData.Load(begin, end);
marketData.Generate();
Assert.AreEqual(22, marketData.Points.Count);
// first test the points
IEnumerator <TemporalPoint> itr = marketData.Points.GetEnumerator();
itr.MoveNext();
TemporalPoint point = itr.Current;
Assert.AreEqual(0, point.Sequence);
Assert.AreEqual(4, point.Data.Length);
Assert.AreEqual(174.68, point[0]);
Assert.AreEqual(26.87, point[1]);
Assert.AreEqual(39, (int)(point[2] / 1000000));
Assert.AreEqual(100, (int)(point[3] / 1000000));
itr.MoveNext();
point = itr.Current;
Assert.AreEqual(1, point.Sequence);
Assert.AreEqual(4, point.Data.Length);
Assert.AreEqual(168.18, point[0]);
Assert.AreEqual(25.88, point[1]);
Assert.AreEqual(29, (int)(point[2] / 1000000));
Assert.AreEqual(84, (int)(point[3] / 1000000));
itr.MoveNext();
point = itr.Current;
Assert.AreEqual(2, point.Sequence);
Assert.AreEqual(4, point.Data.Length);
Assert.AreEqual(170.12, point[0]);
Assert.AreEqual(25.98, point[1]);
Assert.AreEqual(18, (int)(point[2] / 1000000));
Assert.AreEqual(37, (int)(point[3] / 1000000));
// now check the actual data
Assert.AreEqual(16, marketData.Data.Count);
Assert.AreEqual(20, marketData.InputNeuronCount);
Assert.AreEqual(1, marketData.OutputNeuronCount);
IEnumerator <IMLDataPair> itr2 = marketData.Data.GetEnumerator();
itr2.MoveNext();
IMLDataPair pair = itr2.Current;
Assert.AreEqual(20, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(-0.037, Math.Round(pair.Input[0] * 1000.0) / 1000.0);
Assert.AreEqual(-0.037, Math.Round(pair.Input[1] * 1000.0) / 1000.0);
Assert.AreEqual(-0.246, Math.Round(pair.Input[2] * 1000.0) / 1000.0);
Assert.AreEqual(-0.156, Math.Round(pair.Input[3] * 1000.0) / 1000.0);
Assert.AreEqual(0.012, Math.Round(pair.Input[4] * 1000.0) / 1000.0);
Assert.AreEqual(0.0040, Math.Round(pair.Input[5] * 1000.0) / 1000.0);
Assert.AreEqual(-0.375, Math.Round(pair.Input[6] * 1000.0) / 1000.0);
Assert.AreEqual(-0.562, Math.Round(pair.Input[7] * 1000.0) / 1000.0);
Assert.AreEqual(0.03, Math.Round(pair.Input[8] * 1000.0) / 1000.0);
Assert.AreEqual(0.0020, Math.Round(pair.Input[9] * 1000.0) / 1000.0);
Assert.AreEqual(0.57, Math.Round(pair.Input[10] * 100.0) / 100.0);
Assert.AreEqual(0.929, Math.Round(pair.Input[11] * 1000.0) / 1000.0);
Assert.AreEqual(0.025, Math.Round(pair.Input[12] * 1000.0) / 1000.0);
Assert.AreEqual(-0.0070, Math.Round(pair.Input[13] * 1000.0) / 1000.0);
// for some reason, Yahoo likes to vary the volume numbers slightly, sometimes!
Assert.AreEqual(0.1, Math.Round(pair.Input[14] * 10.0) / 10.0);
Assert.AreEqual(-0.084, Math.Round(pair.Input[15] * 1000.0) / 1000.0);
Assert.AreEqual(-0.03, Math.Round(pair.Input[16] * 1000.0) / 1000.0);
Assert.AreEqual(-0.024, Math.Round(pair.Input[17] * 1000.0) / 1000.0);
Assert.AreEqual(0.008, Math.Round(pair.Input[18] * 1000.0) / 1000.0);
Assert.AreEqual(-0.172, Math.Round(pair.Input[19] * 1000.0) / 1000.0);
itr2.MoveNext();
pair = itr2.Current;
Assert.AreEqual(20, pair.Input.Count);
Assert.AreEqual(1, pair.Ideal.Count);
Assert.AreEqual(0.012, Math.Round(pair.Input[0] * 1000.0) / 1000.0);
Assert.AreEqual(0.0040, Math.Round(pair.Input[1] * 1000.0) / 1000.0);
Assert.AreEqual(-0.375, Math.Round(pair.Input[2] * 1000.0) / 1000.0);
Assert.AreEqual(-0.562, Math.Round(pair.Input[3] * 1000.0) / 1000.0);
Assert.AreEqual(0.03, Math.Round(pair.Input[4] * 1000.0) / 1000.0);
Assert.AreEqual(0.0020, Math.Round(pair.Input[5] * 1000.0) / 1000.0);
Assert.AreEqual(0.6, Math.Round(pair.Input[6] * 10.0) / 10.0);
Assert.AreEqual(0.929, Math.Round(pair.Input[7] * 1000.0) / 1000.0);
Assert.AreEqual(0.025, Math.Round(pair.Input[8] * 1000.0) / 1000.0);
Assert.AreEqual(-0.0070, Math.Round(pair.Input[9] * 1000.0) / 1000.0);
Assert.AreEqual(0.1, Math.Round(pair.Input[10] * 10.0) / 10.0);
Assert.AreEqual(-0.084, Math.Round(pair.Input[11] * 1000.0) / 1000.0);
Assert.AreEqual(-0.03, Math.Round(pair.Input[12] * 1000.0) / 1000.0);
Assert.AreEqual(-0.024, Math.Round(pair.Input[13] * 1000.0) / 1000.0);
Assert.AreEqual(0.0080, Math.Round(pair.Input[14] * 1000.0) / 1000.0);
Assert.AreEqual(-0.172, Math.Round(pair.Input[15] * 1000.0) / 1000.0);
Assert.AreEqual(0.014, Math.Round(pair.Input[16] * 1000.0) / 1000.0);
Assert.AreEqual(0.0090, Math.Round(pair.Input[17] * 1000.0) / 1000.0);
Assert.AreEqual(-0.1, Math.Round(pair.Input[18] * 10.0) / 10.0);
Assert.AreEqual(0.066, Math.Round(pair.Input[19] * 1000.0) / 1000.0);
}