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public Load ( TickerSymbol ticker, IList |
||
| ticker | TickerSymbol | The ticker symbol to load. |
| dataNeeded | IList |
The financial data needed. |
| from | System.DateTime | The beginning date to load data from. |
| to | System.DateTime | The ending date to load data to. |
| Résultat | ICollection |
|
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));
}
/// <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();
TickerSymbol 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);
List<LoadedMarketData> results = (List<LoadedMarketData>)loader.Load(ticker, dataNeeded, from, to);
results.Sort();
for (int index = PredictWindow; index < results.Count - EvalWindow; index++)
{
LoadedMarketData data = results[index];
// determine bull or bear position, or neither
bool bullish = false;
bool bearish = false;
for (int search = 1; search <= EvalWindow; search++)
{
LoadedMarketData data2 = results[index + search];
double priceBase = data.GetData(MarketDataType.AdjustedClose);
double priceCompare = data2.GetData(MarketDataType.AdjustedClose);
double diff = priceCompare - priceBase;
double 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);
}
}
}
/// <summary>
/// Load the market data.
/// </summary>
/// <returns>True if the data was loaded.</returns>
private bool LoadMarketData()
{
try
{
IMarketLoader loader = new YahooFinanceLoader();
TickerSymbol ticker = new TickerSymbol(this.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 = this.starting -TimeSpan.FromDays(365);
DateTime to = this.starting + TimeSpan.FromDays(365*2);
this.marketData = (List<LoadedMarketData>)loader.Load(ticker, needed, from, to);
this.marketData.Sort();
this.numberOfDays = (int)((ActualWidth - FIRST_DAY_OFFSET) / DAY_WIDTH);
this.numberOfDays = Math.Min(numberOfDays, this.marketData.Count);
return true;
}
catch (Exception e)
{
MessageBox.Show("Ticker symbol likely invalid.\n"+e.Message, "Error Loading Data");
return false;
}
}
