public Load ( TickerSymbol ticker, IList |
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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. |
return | 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; } }