public void Optimise(DateTime fromDate, DateTime toDate)
{
_stakingService.Evaluate(fromDate, toDate);
Dictionary <int, List <int> > histogram = new();
var history = _marketDataCache.TakeUntil(toDate).ToArray();
for (var i = 1; i < history.Length; i++)
{
var previousPrice = Convert.ToInt32(history[i - 1].DeltaPercent);
var currentPrice = Convert.ToInt32(history[i].DeltaPercent);
if (!histogram.ContainsKey(previousPrice))
{
histogram[previousPrice] = new List <int>();
}
histogram[previousPrice].Add(currentPrice);
}
var parameters = Enumerable.Range(1, 100)
.Select(x => new ProbabilityParameters {
Threshold = -x, Histogram = histogram
});
var optimal = _searcher.Maximum(parameters, fromDate, toDate);
_parameters = (ProbabilityParameters)optimal;
}
public IEnumerable <SimulationState> Evaluate(
IStrategy strategy,
Investor investor,
DateTime?endDate = null,
ProgressBar _ = null)
{
var state = new SimulationState();
foreach (var data in _dataCache.TakeUntil(endDate))
{
var shouldBuy = _simulationCache.GetOrCreate((strategy, data.Date), () => ShouldBuy(strategy, data));
state = state.UpdateState(data, shouldBuy);
state.AddFunds(investor.DailyFunds);
state.ExecuteOrders();
if (state.ShouldBuy)
{
var funds = strategy.GetStake(data.Date, state.TotalFunds);
state.AddBuyOrder(investor.OrderBrokerage, investor.OrderDelayDays, funds);
}
yield return(state);
}
}
public void RateMarketData(DateTime?toDate = null)
{
var buyDates = _marketDataCache
.TakeUntil(toDate)
.Skip(_marketDataCache.BacktestingIndex)
.Select(x => x.Date)
.ToDictionary(k => k, _ => true);
using var progressBar = ProgressBarProvider.Create(_marketDataCache.BacktestingIndex, "Initialising...");
_marketAverage = SimulateBuyDates(buyDates);
_marketMaximum = GetMarketMaximum(buyDates, progressBar);
}
public bool ShouldBuy(MarketData data)
{
var batch = _marketDataCache.TakeUntil(data.Date).ToList().Last(_parameters.Window);
if (batch.Length < 2)
{
return(false);
}
var xData = batch.Select(x => (double)x.Price).ToArray();
var yData = Enumerable.Range(0, batch.Length).Select(x => (double)x).ToArray();
var(intercept, gradient) = Fit.Line(xData, yData);
return(gradient < (double)_parameters.Threshold);
}
public void Optimise(DateTime fromDate, DateTime toDate)
{
_stakingService.Evaluate(fromDate, toDate);
var history = _marketDataCache.TakeUntil(toDate).ToArray();
// Build 2d array where each cell is sized to has equal data points and probability
var xData = history.Select(_xSelector).ToArray();
var yData = history.Select(_ySelector).ToArray();
var grid = new Grid <List <decimal> >(xData, yData, _parameters.Partitions);
// Place each data point in appropriate cell
var marketDataValues = _ratingService.CalculateMarketDataValues(history);
foreach (var data in history)
{
var x = _xSelector(data);
var y = _ySelector(data);
var cell = grid[x, y];
marketDataValues.TryGetValue(data.Date, out var value);
cell.Add(value);
}
// Determine the average value of each cell
var averages = grid
.Where(x => x.Any())
.Select(GetAverage)
.Distinct()
.ToArray();
var minValue = Convert.ToInt32(Math.Round(averages.Min(), MidpointRounding.ToNegativeInfinity));
var maxValue = Convert.ToInt32(Math.Round(averages.Max(), MidpointRounding.ToPositiveInfinity));
var range = maxValue - minValue;
// Search for optimal buy threshold
var potentials = Enumerable.Range(minValue, range)
.SelectMany(t => Enumerable.Range(2, 8)
.Select(p => new ClusteringParameters
{
Partitions = p,
Threshold = -t,
Grid = grid
}));
var optimal = _searcher.Maximum(potentials, fromDate, toDate);
_parameters = (ClusteringParameters)optimal;
}