public Candlestick(
string symbol,
double open,
double high,
double low,
double close,
DateTime time,
int volume = 0)
{
Id = CN.InMemoryId();
Symbol = symbol;
Time = time;
SetOHLCV(open, high, low, close, volume);
}
private void BLearning()
{
while (true)
{
if (Historical.Count < 20000)
{
//
Thread.Sleep(TimeSpan.FromMinutes(30));
}
var correct = 0.0;
var total = 0.0;
var options = new AgentOptions
{
Gamma = Tembo.Random(0.01, 0.99),
Epsilon = Tembo.Random(0.01, 0.75),
Alpha = Tembo.Random(0.01, 0.99),
ExperinceAddEvery = Tembo.RandomInt(1, 10000),
ExperienceSize = 0,
LearningSteps = Tembo.RandomInt(1, 10),
HiddenUnits = Tembo.RandomInt(100000, 100000000),
ErrorClamp = Tembo.Random(0.01, 1.0),
AdaptiveLearningSteps = true
};
var agent = new DQN(dqnAgent.NumberOfStates, dqnAgent.NumberOfActions, options);
for (var i = 0; i < Historical.Count; i++)
{
var spi = Historical.ElementAt(i);
var action = agent.Act(spi.Value.Values);
if (action == spi.Value.Output)
{
correct += 1;
agent.Learn(1);
}
else
{
agent.Learn(-1);
}
total += 1;
}
var winrate = (correct / total) * 100;
if (winrate > WinRate)
{
CN.Log($"NEW AGENT DISCOVERED --> WINRATE {winrate.ToString("p")}, CLASS: {AgentName}", 2);
Save();
dqnAgent = agent;
WinRate = winrate;
}
}
}
