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Fix genetic backend

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This commit is contained in:
cryptooda
2025-07-11 14:11:41 +07:00
parent d04d8f310d
commit e43a1af5ef
15 changed files with 542 additions and 205 deletions
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13
src/Managing.Application/Backtesting/Backtester.cs
View File

@@ -80,8 +80,7 @@ namespace Managing.Application.Backtesting
bool withCandles = false,
string requestId = null)
{
var account = await GetAccountFromConfig(config);
var candles = GetCandles(account, config.Ticker, config.Timeframe, startDate, endDate);
var candles = GetCandles(config.Ticker, config.Timeframe, startDate, endDate);
var result = await RunBacktestWithCandles(config, candles, user, withCandles, requestId);
@@ -165,7 +164,7 @@ namespace Managing.Application.Backtesting
};
}
private List<Candle> GetCandles(Account account, Ticker ticker, Timeframe timeframe,
private List<Candle> GetCandles(Ticker ticker, Timeframe timeframe,
DateTime startDate, DateTime endDate)
{
var candles = _exchangeService.GetCandlesInflux(TradingExchanges.Evm, ticker,
@@ -400,6 +399,12 @@ namespace Managing.Application.Backtesting
return backtests;
}
public IEnumerable<Backtest> GetBacktestsByRequestId(string requestId)
{
var backtests = _backtestRepository.GetBacktestsByRequestId(requestId).ToList();
return backtests;
}
public Backtest GetBacktestByIdForUser(User user, string id)
{
var backtest = _backtestRepository.GetBacktestByIdForUser(user, id);
@@ -462,7 +467,5 @@ namespace Managing.Application.Backtesting
return false;
}
}
}
}

505
src/Managing.Application/GeneticService.cs
View File

@@ -42,104 +42,127 @@ public class GeneticService : IGeneticService
[IndicatorType.EmaTrend] = new() { ["period"] = 14.0 },
[IndicatorType.StDev] = new() { ["period"] = 14.0 },
[IndicatorType.ThreeWhiteSoldiers] = new() { ["period"] = 14.0 },
[IndicatorType.MacdCross] = new() {
["fastPeriods"] = 12.0,
["slowPeriods"] = 26.0,
["signalPeriods"] = 9.0
[IndicatorType.MacdCross] = new()
{
["fastPeriods"] = 12.0,
["slowPeriods"] = 26.0,
["signalPeriods"] = 9.0
},
[IndicatorType.DualEmaCross] = new() {
["fastPeriods"] = 12.0,
["slowPeriods"] = 26.0
[IndicatorType.DualEmaCross] = new()
{
["fastPeriods"] = 12.0,
["slowPeriods"] = 26.0
},
[IndicatorType.SuperTrend] = new() {
["period"] = 14.0,
["multiplier"] = 3.0
[IndicatorType.SuperTrend] = new()
{
["period"] = 14.0,
["multiplier"] = 3.0
},
[IndicatorType.SuperTrendCrossEma] = new() {
["period"] = 14.0,
["multiplier"] = 3.0
[IndicatorType.SuperTrendCrossEma] = new()
{
["period"] = 14.0,
["multiplier"] = 3.0
},
[IndicatorType.ChandelierExit] = new() {
["period"] = 14.0,
["multiplier"] = 3.0
[IndicatorType.ChandelierExit] = new()
{
["period"] = 14.0,
["multiplier"] = 3.0
},
[IndicatorType.StochRsiTrend] = new() {
["period"] = 14.0,
["stochPeriods"] = 14.0,
["signalPeriods"] = 9.0,
["smoothPeriods"] = 3.0
[IndicatorType.StochRsiTrend] = new()
{
["period"] = 14.0,
["stochPeriods"] = 14.0,
["signalPeriods"] = 9.0,
["smoothPeriods"] = 3.0
},
[IndicatorType.Stc] = new() {
["cyclePeriods"] = 10.0,
["fastPeriods"] = 12.0,
["slowPeriods"] = 26.0
[IndicatorType.Stc] = new()
{
["cyclePeriods"] = 10.0,
["fastPeriods"] = 12.0,
["slowPeriods"] = 26.0
},
[IndicatorType.LaggingStc] = new() {
["cyclePeriods"] = 10.0,
["fastPeriods"] = 12.0,
["slowPeriods"] = 26.0
[IndicatorType.LaggingStc] = new()
{
["cyclePeriods"] = 10.0,
["fastPeriods"] = 12.0,
["slowPeriods"] = 26.0
}
};
// Indicator-specific parameter ranges
public static readonly Dictionary<IndicatorType, Dictionary<string, (double min, double max)>> IndicatorParameterRanges = new()
{
[IndicatorType.RsiDivergence] = new() {
["period"] = (5.0, 50.0)
},
[IndicatorType.RsiDivergenceConfirm] = new() {
["period"] = (5.0, 50.0)
},
[IndicatorType.EmaCross] = new() {
["period"] = (5.0, 200.0)
},
[IndicatorType.EmaTrend] = new() {
["period"] = (5.0, 200.0)
},
[IndicatorType.StDev] = new() {
["period"] = (5.0, 50.0)
},
[IndicatorType.ThreeWhiteSoldiers] = new() {
["period"] = (5.0, 50.0)
},
[IndicatorType.MacdCross] = new() {
["fastPeriods"] = (10.0, 50.0),
["slowPeriods"] = (20.0, 100.0),
["signalPeriods"] = (5.0, 20.0)
},
[IndicatorType.DualEmaCross] = new() {
["fastPeriods"] = (5.0, 300.0),
["slowPeriods"] = (5.0, 300.0)
},
[IndicatorType.SuperTrend] = new() {
["period"] = (5.0, 50.0),
["multiplier"] = (1.0, 10.0)
},
[IndicatorType.SuperTrendCrossEma] = new() {
["period"] = (5.0, 50.0),
["multiplier"] = (1.0, 10.0)
},
[IndicatorType.ChandelierExit] = new() {
["period"] = (5.0, 50.0),
["multiplier"] = (1.0, 10.0)
},
[IndicatorType.StochRsiTrend] = new() {
["period"] = (5.0, 50.0),
["stochPeriods"] = (5.0, 30.0),
["signalPeriods"] = (3.0, 15.0),
["smoothPeriods"] = (1.0, 10.0)
},
[IndicatorType.Stc] = new() {
["cyclePeriods"] = (5.0, 30.0),
["fastPeriods"] = (5.0, 50.0),
["slowPeriods"] = (10.0, 100.0)
},
[IndicatorType.LaggingStc] = new() {
["cyclePeriods"] = (5.0, 30.0),
["fastPeriods"] = (5.0, 50.0),
["slowPeriods"] = (10.0, 100.0)
}
};
public static readonly Dictionary<IndicatorType, Dictionary<string, (double min, double max)>>
IndicatorParameterRanges = new()
{
[IndicatorType.RsiDivergence] = new()
{
["period"] = (5.0, 50.0)
},
[IndicatorType.RsiDivergenceConfirm] = new()
{
["period"] = (5.0, 50.0)
},
[IndicatorType.EmaCross] = new()
{
["period"] = (5.0, 200.0)
},
[IndicatorType.EmaTrend] = new()
{
["period"] = (5.0, 200.0)
},
[IndicatorType.StDev] = new()
{
["period"] = (5.0, 50.0)
},
[IndicatorType.ThreeWhiteSoldiers] = new()
{
["period"] = (5.0, 50.0)
},
[IndicatorType.MacdCross] = new()
{
["fastPeriods"] = (10.0, 50.0),
["slowPeriods"] = (20.0, 100.0),
["signalPeriods"] = (5.0, 20.0)
},
[IndicatorType.DualEmaCross] = new()
{
["fastPeriods"] = (5.0, 300.0),
["slowPeriods"] = (5.0, 300.0)
},
[IndicatorType.SuperTrend] = new()
{
["period"] = (5.0, 50.0),
["multiplier"] = (1.0, 10.0)
},
[IndicatorType.SuperTrendCrossEma] = new()
{
["period"] = (5.0, 50.0),
["multiplier"] = (1.0, 10.0)
},
[IndicatorType.ChandelierExit] = new()
{
["period"] = (5.0, 50.0),
["multiplier"] = (1.0, 10.0)
},
[IndicatorType.StochRsiTrend] = new()
{
["period"] = (5.0, 50.0),
["stochPeriods"] = (5.0, 30.0),
["signalPeriods"] = (3.0, 15.0),
["smoothPeriods"] = (1.0, 10.0)
},
[IndicatorType.Stc] = new()
{
["cyclePeriods"] = (5.0, 30.0),
["fastPeriods"] = (5.0, 50.0),
["slowPeriods"] = (10.0, 100.0)
},
[IndicatorType.LaggingStc] = new()
{
["cyclePeriods"] = (5.0, 30.0),
["fastPeriods"] = (5.0, 50.0),
["slowPeriods"] = (10.0, 100.0)
}
};
// Indicator type to parameter mapping
public static readonly Dictionary<IndicatorType, string[]> IndicatorParamMapping = new()
@@ -236,8 +259,10 @@ public class GeneticService : IGeneticService
/// Runs the genetic algorithm for a specific request
/// </summary>
/// <param name="request">The genetic request to process</param>
/// <param name="cancellationToken">Cancellation token to stop the algorithm</param>
/// <returns>The genetic algorithm result</returns>
public async Task<GeneticAlgorithmResult> RunGeneticAlgorithm(GeneticRequest request)
public async Task<GeneticAlgorithmResult> RunGeneticAlgorithm(GeneticRequest request,
CancellationToken cancellationToken = default)
{
try
{
@@ -247,15 +272,39 @@ public class GeneticService : IGeneticService
request.Status = GeneticRequestStatus.Running;
UpdateGeneticRequest(request);
// Create chromosome for trading bot configuration
var chromosome = new TradingBotChromosome(request.EligibleIndicators, request.MaxTakeProfit);
// Create or resume chromosome for trading bot configuration
TradingBotChromosome chromosome;
Population population;
if (!string.IsNullOrEmpty(request.BestChromosome) && request.CurrentGeneration > 0)
{
// Resume from previous state (best chromosome only)
chromosome = new TradingBotChromosome(request.EligibleIndicators, request.MaxTakeProfit);
var savedChromosome = JsonSerializer.Deserialize<double[]>(request.BestChromosome);
if (savedChromosome != null)
{
chromosome.ReplaceGenes(0, savedChromosome.Select(g => new Gene(g)).ToArray());
}
population = new Population(request.PopulationSize, request.PopulationSize, chromosome);
_logger.LogInformation(
"Resuming genetic algorithm for request {RequestId} from generation {Generation} with best chromosome",
request.RequestId, request.CurrentGeneration);
}
else
{
// Start fresh
chromosome = new TradingBotChromosome(request.EligibleIndicators, request.MaxTakeProfit);
population = new Population(request.PopulationSize, request.PopulationSize, chromosome);
_logger.LogInformation("Starting fresh genetic algorithm for request {RequestId}", request.RequestId);
}
// Create fitness function
var fitness = new TradingBotFitness(_backtester, request);
// Create genetic algorithm with better configuration
var ga = new GeneticAlgorithm(
new Population(request.PopulationSize, request.PopulationSize, chromosome),
population,
fitness,
GetSelection(request.SelectionMethod),
new UniformCrossover(),
@@ -266,14 +315,73 @@ public class GeneticService : IGeneticService
CrossoverProbability = 0.7f // Fixed crossover rate as in frontend
};
// Custom termination condition that checks for cancellation
var originalTermination = ga.Termination;
ga.Termination = new GenerationNumberTermination(request.Generations);
// Add cancellation check in the generation event
// Run the genetic algorithm with periodic checks for cancellation
var generationCount = 0;
ga.GenerationRan += (sender, e) =>
{
generationCount = ga.GenerationsNumber;
// Update progress every 5 generations
if (generationCount % 5 == 0)
{
var bestFitness = ga.BestChromosome?.Fitness ?? 0;
request.CurrentGeneration = generationCount;
request.BestFitnessSoFar = bestFitness;
if (ga.BestChromosome is TradingBotChromosome bestChromosome)
{
var genes = bestChromosome.GetGenes();
var geneValues = genes.Select(g =>
{
if (g.Value is double doubleValue) return doubleValue;
if (g.Value is int intValue) return (double)intValue;
return Convert.ToDouble(g.Value.ToString());
}).ToArray();
request.BestChromosome = JsonSerializer.Serialize(geneValues);
}
UpdateGeneticRequest(request);
}
// Check for cancellation
if (cancellationToken.IsCancellationRequested)
{
ga.Stop();
}
};
// Run the genetic algorithm
ga.Start();
// Check if the algorithm was cancelled
if (cancellationToken.IsCancellationRequested)
{
_logger.LogInformation("Genetic algorithm cancelled for request {RequestId}", request.RequestId);
// Update request status to pending so it can be resumed
request.Status = GeneticRequestStatus.Pending;
UpdateGeneticRequest(request);
return new GeneticAlgorithmResult
{
BestFitness = request.BestFitnessSoFar ?? 0,
BestIndividual = request.BestIndividual ?? "unknown",
ProgressInfo = request.ProgressInfo,
CompletedAt = DateTime.UtcNow
};
}
// Get the best chromosome
var bestChromosome = ga.BestChromosome as TradingBotChromosome;
var bestFitness = ga.BestChromosome.Fitness.Value;
var bestFitness = ga.BestChromosome?.Fitness ?? 0;
_logger.LogInformation("Genetic algorithm completed for request {RequestId}. Best fitness: {Fitness}",
_logger.LogInformation("Genetic algorithm completed for request {RequestId}. Best fitness: {Fitness}",
request.RequestId, bestFitness);
// Update request with results
@@ -289,7 +397,7 @@ public class GeneticService : IGeneticService
generations = request.Generations,
completed_at = DateTime.UtcNow
});
UpdateGeneticRequest(request);
return new GeneticAlgorithmResult
@@ -303,13 +411,13 @@ public class GeneticService : IGeneticService
catch (Exception ex)
{
_logger.LogError(ex, "Error running genetic algorithm for request {RequestId}", request.RequestId);
// Update request with error
request.Status = GeneticRequestStatus.Failed;
request.ErrorMessage = ex.Message;
request.CompletedAt = DateTime.UtcNow;
UpdateGeneticRequest(request);
throw;
}
}
@@ -345,11 +453,18 @@ public class TradingBotChromosome : ChromosomeBase
// 3-6: Indicator selection (up to 4 indicators)
// 7+: Indicator parameters (period, fastPeriods, etc.)
public TradingBotChromosome(List<IndicatorType> eligibleIndicators, double maxTakeProfit)
: base(4 + 4 + eligibleIndicators.Count * 8) // Trading params + indicator selection + indicator params
public TradingBotChromosome(List<IndicatorType> eligibleIndicators, double maxTakeProfit)
: base(4 + 1 + 4 +
eligibleIndicators.Count * 8) // Trading params + loopback + indicator selection + indicator params
{
_eligibleIndicators = eligibleIndicators;
_maxTakeProfit = maxTakeProfit;
// Initialize all genes
for (int i = 0; i < Length; i++)
{
ReplaceGene(i, GenerateGene(i));
}
}
public override Gene GenerateGene(int geneIndex)
@@ -366,7 +481,12 @@ public class TradingBotChromosome : ChromosomeBase
_ => new Gene(0)
};
}
else if (geneIndex < 8)
else if (geneIndex == 4)
{
// LoopbackPeriod gene (always between 5 and 20)
return new Gene(GetRandomIntInRange((5, 20)));
}
else if (geneIndex < 9)
{
// Indicator selection (0 = not selected, 1 = selected)
return new Gene(_random.Next(2));
@@ -374,46 +494,46 @@ public class TradingBotChromosome : ChromosomeBase
else
{
// Indicator parameters
var indicatorIndex = (geneIndex - 8) / 8;
var paramIndex = (geneIndex - 8) % 8;
var indicatorIndex = (geneIndex - 9) / 8;
var paramIndex = (geneIndex - 9) % 8;
if (indicatorIndex < _eligibleIndicators.Count)
{
var indicator = _eligibleIndicators[indicatorIndex];
var paramName = GetParameterName(paramIndex);
if (paramName != null && GeneticService.IndicatorParamMapping.ContainsKey(indicator))
{
var requiredParams = GeneticService.IndicatorParamMapping[indicator];
if (requiredParams.Contains(paramName))
{
// Use indicator-specific ranges only
if (GeneticService.IndicatorParameterRanges.ContainsKey(indicator) &&
GeneticService.IndicatorParameterRanges[indicator].ContainsKey(paramName))
{
var indicatorRange = GeneticService.IndicatorParameterRanges[indicator][paramName];
// 70% chance to use default value, 30% chance to use random value within indicator-specific range
if (_random.NextDouble() < 0.7)
{
var defaultValues = GeneticService.DefaultIndicatorValues[indicator];
return new Gene(defaultValues[paramName]);
}
else
{
return new Gene(GetRandomInRange(indicatorRange));
}
}
else
{
// If no indicator-specific range is found, use default value only
var defaultValues = GeneticService.DefaultIndicatorValues[indicator];
return new Gene(defaultValues[paramName]);
}
}
}
if (paramName != null && GeneticService.IndicatorParamMapping.ContainsKey(indicator))
{
var requiredParams = GeneticService.IndicatorParamMapping[indicator];
if (requiredParams.Contains(paramName))
{
// Use indicator-specific ranges only
if (GeneticService.IndicatorParameterRanges.ContainsKey(indicator) &&
GeneticService.IndicatorParameterRanges[indicator].ContainsKey(paramName))
{
var indicatorRange = GeneticService.IndicatorParameterRanges[indicator][paramName];
// 70% chance to use default value, 30% chance to use random value within indicator-specific range
if (_random.NextDouble() < 0.7)
{
var defaultValues = GeneticService.DefaultIndicatorValues[indicator];
return new Gene(defaultValues[paramName]);
}
else
{
return new Gene(GetRandomInRange(indicatorRange));
}
}
else
{
// If no indicator-specific range is found, use default value only
var defaultValues = GeneticService.DefaultIndicatorValues[indicator];
return new Gene(defaultValues[paramName]);
}
}
}
}
return new Gene(0);
}
}
@@ -434,70 +554,70 @@ public class TradingBotChromosome : ChromosomeBase
{
var selected = new List<GeneticIndicator>();
var genes = GetGenes();
for (int i = 0; i < 4; i++) // Check first 4 indicator slots
{
if (genes[4 + i].Value.ToString() == "1" && i < _eligibleIndicators.Count)
if (genes[5 + i].Value.ToString() == "1" && i < _eligibleIndicators.Count)
{
var indicator = new GeneticIndicator
{
Type = _eligibleIndicators[i]
};
// Add parameters for this indicator
var baseIndex = 8 + i * 8;
var baseIndex = 9 + i * 8;
var paramName = GetParameterName(0); // period
if (paramName != null && HasParameter(_eligibleIndicators[i], paramName))
{
indicator.Period = Convert.ToInt32(genes[baseIndex].Value);
}
paramName = GetParameterName(1); // fastPeriods
if (paramName != null && HasParameter(_eligibleIndicators[i], paramName))
{
indicator.FastPeriods = Convert.ToInt32(genes[baseIndex + 1].Value);
}
paramName = GetParameterName(2); // slowPeriods
if (paramName != null && HasParameter(_eligibleIndicators[i], paramName))
{
indicator.SlowPeriods = Convert.ToInt32(genes[baseIndex + 2].Value);
}
paramName = GetParameterName(3); // signalPeriods
if (paramName != null && HasParameter(_eligibleIndicators[i], paramName))
{
indicator.SignalPeriods = Convert.ToInt32(genes[baseIndex + 3].Value);
}
paramName = GetParameterName(4); // multiplier
if (paramName != null && HasParameter(_eligibleIndicators[i], paramName))
{
indicator.Multiplier = Convert.ToDouble(genes[baseIndex + 4].Value);
}
paramName = GetParameterName(5); // stochPeriods
if (paramName != null && HasParameter(_eligibleIndicators[i], paramName))
{
indicator.StochPeriods = Convert.ToInt32(genes[baseIndex + 5].Value);
}
paramName = GetParameterName(6); // smoothPeriods
if (paramName != null && HasParameter(_eligibleIndicators[i], paramName))
{
indicator.SmoothPeriods = Convert.ToInt32(genes[baseIndex + 6].Value);
}
paramName = GetParameterName(7); // cyclePeriods
if (paramName != null && HasParameter(_eligibleIndicators[i], paramName))
{
indicator.CyclePeriods = Convert.ToInt32(genes[baseIndex + 7].Value);
}
selected.Add(indicator);
}
}
return selected;
}
@@ -505,7 +625,7 @@ public class TradingBotChromosome : ChromosomeBase
{
var genes = GetGenes();
var selectedIndicators = GetSelectedIndicators();
// Ensure we have at least one indicator
if (!selectedIndicators.Any())
{
@@ -514,25 +634,28 @@ public class TradingBotChromosome : ChromosomeBase
// Get take profit from chromosome (gene 0)
var takeProfit = Convert.ToDouble(genes[0].Value);
// Calculate stop loss based on 1.1:1 risk-reward ratio (gene 1)
var stopLoss = Convert.ToDouble(genes[1].Value);
// Ensure minimum 1.1:1 risk-reward ratio and minimum 0.2% to cover fees
var minStopLossForRR = takeProfit / 1.1;
var minStopLossForFees = 0.2; // Minimum 0.2% to cover trading fees
var minStopLoss = Math.Max(minStopLossForRR, minStopLossForFees);
var maxStopLoss = takeProfit - 0.1; // Ensure SL is less than TP with some buffer
// Adjust stop loss if it doesn't meet the constraints
if (stopLoss > maxStopLoss || stopLoss < minStopLoss)
{
stopLoss = GetRandomInRange((minStopLoss, maxStopLoss));
}
// Get loopback period from gene 4
var loopbackPeriod = Convert.ToInt32(genes[4].Value);
// Build scenario using selected indicators
var scenario = new Scenario($"Genetic_{request.RequestId}_Scenario", 1);
var scenario = new Scenario($"Genetic_{request.RequestId}_Scenario", loopbackPeriod);
foreach (var geneticIndicator in selectedIndicators)
{
var indicator = ScenarioHelpers.BuildIndicator(
@@ -547,14 +670,14 @@ public class TradingBotChromosome : ChromosomeBase
smoothPeriods: geneticIndicator.SmoothPeriods,
cyclePeriods: geneticIndicator.CyclePeriods
);
scenario.AddIndicator(indicator);
}
return new TradingBotConfig
{
Name = $"Genetic_{request.RequestId}",
AccountName = "genetic_account",
AccountName = "Oda-embedded",
Ticker = request.Ticker,
Timeframe = request.Timeframe,
BotTradingBalance = request.Balance,
@@ -612,11 +735,11 @@ public class TradingBotChromosome : ChromosomeBase
};
}
private bool HasParameter(IndicatorType indicator, string paramName)
{
return GeneticService.IndicatorParamMapping.ContainsKey(indicator) &&
GeneticService.IndicatorParamMapping[indicator].Contains(paramName);
}
private bool HasParameter(IndicatorType indicator, string paramName)
{
return GeneticService.IndicatorParamMapping.ContainsKey(indicator) &&
GeneticService.IndicatorParamMapping[indicator].Contains(paramName);
}
}
/// <summary>
@@ -658,21 +781,21 @@ public class TradingBotFitness : IFitness
return 0;
var config = tradingBotChromosome.GetTradingBotConfig(_request);
// Run backtest
var backtest = _backtester.RunTradingBotBacktest(
config,
_request.StartDate,
_request.EndDate,
_request.User,
false, // Don't save individual backtests
true, // Don't save individual backtests
false, // Don't include candles
_request.RequestId
).Result;
// Calculate multi-objective fitness based on backtest results
var fitness = CalculateMultiObjectiveFitness(backtest, config);
return fitness;
}
catch (Exception)
@@ -688,30 +811,32 @@ public class TradingBotFitness : IFitness
return 0.1;
var stats = backtest.Statistics;
// Multi-objective fitness function (matching frontend)
var pnlScore = Math.Max(0, (double)stats.TotalPnL / 1000); // Normalize PnL
var winRateScore = backtest.WinRate / 100.0; // Normalize win rate
var riskRewardScore = Math.Min(2, (double)stats.WinningTrades / Math.Max(1, Math.Abs((double)stats.LoosingTrades)));
var consistencyScore = 1 - Math.Abs((double)stats.TotalPnL - (double)backtest.FinalPnl) / Math.Max(1, Math.Abs((double)stats.TotalPnL));
// Risk-reward ratio bonus
var riskRewardRatio = (double)(config.MoneyManagement.TakeProfit / config.MoneyManagement.StopLoss);
var riskRewardBonus = Math.Min(0.2, (riskRewardRatio - 1.1) * 0.1);
// Drawdown score (normalized to 0-1, where lower drawdown is better)
var maxDrawdownPc = Math.Abs((double)stats.MaxDrawdownPc);
var drawdownScore = Math.Max(0, 1 - (maxDrawdownPc / 50));
// Weighted combination
var fitness =
pnlScore * 0.3 +
winRateScore * 0.2 +
riskRewardScore * 0.2 +
consistencyScore * 0.1 +
riskRewardBonus * 0.1 +
drawdownScore * 0.1;
// Multi-objective fitness function (matching frontend)
var pnlScore = Math.Max(0, (double)stats.TotalPnL / 1000); // Normalize PnL
var winRateScore = backtest.WinRate / 100.0; // Normalize win rate
var riskRewardScore =
Math.Min(2, (double)stats.WinningTrades / Math.Max(1, Math.Abs((double)stats.LoosingTrades)));
var consistencyScore = 1 - Math.Abs((double)stats.TotalPnL - (double)backtest.FinalPnl) /
Math.Max(1, Math.Abs((double)stats.TotalPnL));
// Risk-reward ratio bonus
var riskRewardRatio = (double)(config.MoneyManagement.TakeProfit / config.MoneyManagement.StopLoss);
var riskRewardBonus = Math.Min(0.2, (riskRewardRatio - 1.1) * 0.1);
// Drawdown score (normalized to 0-1, where lower drawdown is better)
var maxDrawdownPc = Math.Abs((double)stats.MaxDrawdownPc);
var drawdownScore = Math.Max(0, 1 - (maxDrawdownPc / 50));
// Weighted combination
var fitness =
pnlScore * 0.3 +
winRateScore * 0.2 +
riskRewardScore * 0.2 +
consistencyScore * 0.1 +
riskRewardBonus * 0.1 +
drawdownScore * 0.1;
return Math.Max(0, fitness);
}
}
}