using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis;
using System.Threading;

namespace HeuristicLab.Problems.TradeRules
{
    [StorableClass]
    [Item("Interpreter", "Represents a grammar for Trading Problems")]
    public sealed class Interpreter : ParameterizedNamedItem, ITradeRulesExpresionTree
    {
        private const string CheckExpressionsWithIntervalArithmeticParameterName = "CheckExpressionsWithIntervalArithmetic";
        private const string EvaluatedSolutionsParameterName = "EvaluatedSolutions";
        private int initialTraining;
        private int initialTest;

        [ThreadStatic]
        private static Dictionary<ISymbolicExpressionTreeNode, double> signalCache;
        [ThreadStatic]
        private static Dictionary<ISymbolicExpressionTreeNode, double> firstEMACache;
        [ThreadStatic]
        private static Dictionary<ISymbolicExpressionTreeNode, double> secondEMACache;
        [ThreadStatic]
        private static Dictionary<ISymbolicExpressionTreeNode, double> RSIPositiveCache;
        [ThreadStatic]
        private static Dictionary<ISymbolicExpressionTreeNode, double> RSINegativeCache;
        [ThreadStatic]
        private static Dictionary<ISymbolicExpressionTreeNode, double> RSICache;
        [ThreadStatic]
        private static Dictionary<ISymbolicExpressionTreeNode, double> RSIOutputCache;

        #region private classes
        //This class manipulate the instructions of the stack
        private class InterpreterState
        {
            private double[] argumentStack;
            private int argumentStackPointer;
            private Instruction[] code;
            private int pc;
            public int ProgramCounter
            {
                get { return pc; }
                set { pc = value; }
            }
            internal InterpreterState(Instruction[] code, int argumentStackSize)
            {
                this.code = code;
                this.pc = 0;
                if (argumentStackSize > 0)
                {
                    this.argumentStack = new double[argumentStackSize];
                }
                this.argumentStackPointer = 0;
            }

            internal void Reset()
            {
                this.pc = 0;
                this.argumentStackPointer = 0;
            }

            internal Instruction NextInstruction()
            {
                return code[pc++];
            }
            private void Push(double val)
            {
                argumentStack[argumentStackPointer++] = val;
            }
            private double Pop()
            {
                return argumentStack[--argumentStackPointer];
            }

            internal void CreateStackFrame(double[] argValues)
            {
                // push in reverse order to make indexing easier
                for (int i = argValues.Length - 1; i >= 0; i--)
                {
                    argumentStack[argumentStackPointer++] = argValues[i];
                }
                Push(argValues.Length);
            }

            internal void RemoveStackFrame()
            {
                int size = (int)Pop();
                argumentStackPointer -= size;
            }

            internal double GetStackFrameValue(ushort index)
            {
                // layout of stack:
                // [0]   <- argumentStackPointer
                // [StackFrameSize = N + 1]
                // [Arg0] <- argumentStackPointer - 2 - 0
                // [Arg1] <- argumentStackPointer - 2 - 1
                // [...]
                // [ArgN] <- argumentStackPointer - 2 - N
                // <Begin of stack frame>
                return argumentStack[argumentStackPointer - index - 2];
            }
        }

        //Operation codes
        private class OpCodes
        {
            public const byte Add = 1;
            public const byte Sub = 2;
            public const byte Mul = 3;

            public const byte GT = 5;
            public const byte LT = 6;

            public const byte AND = 7;
            public const byte OR = 8;
            public const byte NOT = 9;
            public const byte BOOLEAN = 10;

            public const byte Average = 11;
            public const byte MACD = 12;

            public const byte Variable = 13;
            public const byte ConstantInt = 16;
            public const byte BoolConstant = 15;
            public const byte Max = 17;
            public const byte Min = 18;
            public const byte Lag = 19;
            public const byte RSI = 20;
        }
        #endregion

        #region IStatefulItem
        public void InitializeState()
        {
            EvaluatedSolutions.Value = 0;
        }

        public void ClearState()
        {
        }
        #endregion

        private Dictionary<Type, byte> symbolToOpcode = new Dictionary<Type, byte>() {
      { typeof(Addition), OpCodes.Add },
      { typeof(Subtraction), OpCodes.Sub },
      { typeof(Multiplication), OpCodes.Mul },
      { typeof(BoolConstant), OpCodes.BoolConstant },
      { typeof(ConstantInt), OpCodes.ConstantInt },
      { typeof(GreaterThan), OpCodes.GT },
      { typeof(LessThan), OpCodes.LT },
      { typeof(And), OpCodes.AND },
      { typeof(Or), OpCodes.OR },
      { typeof(Not), OpCodes.NOT},
      { typeof(AverageTrade), OpCodes.Average},
      { typeof(MACD), OpCodes.MACD},
      { typeof(RSI), OpCodes.RSI},
      { typeof(Max), OpCodes.Max},
      { typeof(Min), OpCodes.Min},
      { typeof(Lag), OpCodes.Lag},
      { typeof(HeuristicLab.Problems.DataAnalysis.Symbolic.Variable), OpCodes.Variable },
    };

        public override bool CanChangeName
        {
            get { return false; }
        }
        public override bool CanChangeDescription
        {
            get { return false; }
        }

        #region parameter properties
        public IValueParameter<BoolValue> CheckExpressionsWithIntervalArithmeticParameter
        {
            get { return (IValueParameter<BoolValue>)Parameters[CheckExpressionsWithIntervalArithmeticParameterName]; }
        }

        public IValueParameter<IntValue> EvaluatedSolutionsParameter
        {
            get { return (IValueParameter<IntValue>)Parameters[EvaluatedSolutionsParameterName]; }
        }
        #endregion

        #region properties
        public BoolValue CheckExpressionsWithIntervalArithmetic
        {
            get { return CheckExpressionsWithIntervalArithmeticParameter.Value; }
            set { CheckExpressionsWithIntervalArithmeticParameter.Value = value; }
        }

        public IntValue EvaluatedSolutions
        {
            get { return EvaluatedSolutionsParameter.Value; }
            set { EvaluatedSolutionsParameter.Value = value; }
        }
        #endregion

        private double Evaluate(Dataset dataset, ref int row, InterpreterState state)
        {
            Instruction currentInstr = state.NextInstruction();

            switch (currentInstr.opCode)
            {
                case OpCodes.Add:
                    {
                        double s = Evaluate(dataset, ref row, state);
                        for (int i = 1; i < currentInstr.nArguments; i++)
                        {
                            s += Evaluate(dataset, ref row, state);
                        }
                        return s;
                    }
                case OpCodes.Sub:
                    {
                        double s = Evaluate(dataset, ref row, state);
                        for (int i = 1; i < currentInstr.nArguments; i++)
                        {
                            s -= Evaluate(dataset, ref row, state);
                        }
                        if (currentInstr.nArguments == 1) s = -s;
                        return s;
                    }
                case OpCodes.Mul:
                    {
                        double p = Evaluate(dataset, ref row, state);
                        for (int i = 1; i < currentInstr.nArguments; i++)
                        {
                            p *= Evaluate(dataset, ref row, state);
                        }
                        return p;
                    }
                case OpCodes.Average:
                    {
                        double sum = Evaluate(dataset, ref row, state);
                        int integerValue = (int)Math.Floor(sum);
                        if (integerValue > 100) integerValue = 100;
                        if (row < integerValue)
                        {
                            string variableName = dataset.GetValue(row, 3);
                            double inferiorValue = Convert.ToDouble(variableName);
                            return inferiorValue / (row + 1);
                        }
                        else
                        {
                            string variableName = dataset.GetValue(row, 3);
                            double meanValue1 = Convert.ToDouble(variableName);
                            string variableName2 = dataset.GetValue((row - integerValue), 3);
                            double meanValue2 = Convert.ToDouble(variableName2);
                            return (meanValue1 - meanValue2) / integerValue;
                        }
                    }
                case OpCodes.AND:
                    {
                        double result = Evaluate(dataset, ref row, state);
                        double result2 = Evaluate(dataset, ref row, state);
                        double total = result + result2;
                        return total < 2.0 ? -1.0 : 1.0;
                    }
                case OpCodes.OR:
                    {
                        double result = Evaluate(dataset, ref row, state);
                        double result2 = Evaluate(dataset, ref row, state);
                        double total = result + result2;
                        return total > -2.0 ? 1.0 : -1.0;
                    }
                case OpCodes.NOT:
                    {
                        return Evaluate(dataset, ref row, state) > -1.0 ? -1.0 : 1.0;
                    }

                case OpCodes.BOOLEAN:
                    {
                        var booleanTreeNode = currentInstr.dynamicNode as BoolConstantTreeNode;
                        return booleanTreeNode.Value;
                    }
                case OpCodes.GT:
                    {
                        double x = Evaluate(dataset, ref row, state);
                        double y = Evaluate(dataset, ref row, state);
                        if (x > y) return 1.0;
                        else return -1.0;
                    }
                case OpCodes.LT:
                    {
                        double x = Evaluate(dataset, ref row, state);
                        double y = Evaluate(dataset, ref row, state);
                        if (x < y) return 1.0;
                        else return -1.0;
                    }
                case OpCodes.Variable:
                    {
                        if (row < 0 || row >= dataset.Rows)
                            return double.NaN;
                        var variableTreeNode = (VariableTreeNode)currentInstr.dynamicNode;
                        return ((IList<double>)currentInstr.iArg0)[row] * variableTreeNode.Weight;
                    }
                case OpCodes.BoolConstant:
                    {
                        var boolConstTreeNode = currentInstr.dynamicNode as BoolConstantTreeNode;
                        return boolConstTreeNode.Value;
                    }
                case OpCodes.ConstantInt:
                    {
                        var constIntTreeNode = currentInstr.dynamicNode as ConstantIntTreeNode;
                        return constIntTreeNode.Value;
                    }
                case OpCodes.Max:
                    {
                        int n = (int)Evaluate(dataset, ref row, state);
                        double max = Double.NegativeInfinity;
                        int i = Math.Min(n, row);
                        while (i >= 0)
                        {
                            int position = row - i;
                            string variableName = dataset.GetValue(position, 2);
                            double intValue = Convert.ToDouble(variableName);
                            if (intValue > max) max = intValue;
                            i--;
                        }
                        return max;
                    }
                case OpCodes.Min:
                    {
                        int n = (int)Evaluate(dataset, ref row, state);
                        double min = Double.NegativeInfinity;
                        int i = Math.Min(n, row);
                        while (i >= 0)
                        {
                            int position = row - i;
                            string variableName = dataset.GetValue(position, 2);
                            double intValue = Convert.ToDouble(variableName);
                            if (intValue < min) min = intValue;
                            i--;
                        }
                        return min;
                    }
                case OpCodes.Lag:
                    {
                        int n = (int)Evaluate(dataset, ref row, state);
                        if (n > row) return 0;
                        int position = row - n;
                        string variableName = dataset.GetValue(position, 2);
                        double intValue = Convert.ToDouble(variableName);
                        return intValue;
                    }
                case OpCodes.MACD:
                    {
                        //Taking the number of the days for each EMA
                        double firstEMA = Evaluate(dataset, ref row, state);
                        double secondEMA = Evaluate(dataset, ref row, state);
                        double signal = Evaluate(dataset, ref row, state);
                        //Initiation of the variables
                        double firstElementEMA = -1000000;
                        double secondElementEMA = -100000;
                        double signalValue = -100000;
                        double macd = 0;

                        //Check if this MACD has previous values and retrieve them
                        if (firstEMACache.ContainsKey(currentInstr.dynamicNode)) firstElementEMA = firstEMACache[currentInstr.dynamicNode];
                        if (secondEMACache.ContainsKey(currentInstr.dynamicNode)) secondElementEMA = secondEMACache[currentInstr.dynamicNode];
                        if (signalCache.ContainsKey(currentInstr.dynamicNode)) signalValue = signalCache[currentInstr.dynamicNode];

                
                        //Calculating the factor for each EMA
                        double factor = 2.0 / (firstEMA + 1.0);
                        double factor2 = 2.0 / (secondEMA + 1.0);
                        double factor3 = 2.0 / (signal + 1.0);

                            //Calculate the first value in the training for the two EMAs and the signal.
                            if (row == initialTest)
                            {
                                double [] meanValues = dataset.GetDoubleValues("\"Close\"", Enumerable.Range(0, row+1)).ToArray();
                                firstElementEMA = meanValues[0];
                                secondElementEMA = meanValues[0];
                                double max = (Math.Max(firstEMA, secondEMA)-1);//The first macd happens when the longest EMA has its first value. We need -1 because row begin in 0.
                                for (int i = 1; i < meanValues.Length; i++)
                                {
                                    firstElementEMA = (meanValues[i] * factor) + ((1 - factor) * firstElementEMA);
                                    secondElementEMA = (meanValues[i] * factor2) + ((1 - factor2) * secondElementEMA);
                                    if (i == max) signalValue = firstElementEMA - secondElementEMA;//First signal equals to macd.
                                    else if (i > max)//Calculation for the next signals
                                    {
                                        macd = firstElementEMA - secondElementEMA;
                                        signalValue = macd * factor3 + (1 - factor3) * signalValue;
                                    }
                                }
                            }
                            else  //The rest of the rows are calculating with the standard EMA formula
                            {
                                //Retrieve the dataset values 
                                string variableName = dataset.GetValue(row, 2);
                                double meanValue1 = Convert.ToDouble(variableName);

                                //Calculating EMA
                                firstElementEMA = meanValue1 * factor + (1 - factor) * firstElementEMA;
                                secondElementEMA = meanValue1 * factor2 + (1 - factor2) * secondElementEMA;

                                //Calculating signal
                                macd = firstElementEMA - secondElementEMA;
                                signalValue = macd * factor3 + (1 - factor3) * signalValue;
                            }
                            
                            //Save the values for the next iteration
                            firstEMACache[currentInstr.dynamicNode] = firstElementEMA;
                            secondEMACache[currentInstr.dynamicNode] = secondElementEMA;
                            signalCache[currentInstr.dynamicNode] = signalValue;
                           
                        return macd > signalValue ? 1.0 : -1.0;
                    }
                case OpCodes.RSI:
                    {
                        //Taking the number of the days for EMA
                        double numberOfDays = Evaluate(dataset, ref row, state);
                        double upThreshold = Evaluate(dataset, ref row, state);
                        double downThreshold = Evaluate(dataset, ref row, state);

                        double positiveEMA = 0;
                        double negativeEMA = 0;
                        double yesterdayRSI = double.NegativeInfinity;
                        double todayRSI = double.NegativeInfinity;
                        double outputRSI = double.NegativeInfinity;
                        //Retrieve EMA values
                        if (RSIPositiveCache.ContainsKey(currentInstr.dynamicNode)) positiveEMA = RSIPositiveCache[currentInstr.dynamicNode];
                        if (RSINegativeCache.ContainsKey(currentInstr.dynamicNode)) negativeEMA = RSINegativeCache[currentInstr.dynamicNode];
                        if (RSICache.ContainsKey(currentInstr.dynamicNode)) yesterdayRSI = RSICache[currentInstr.dynamicNode];
                        if (RSIOutputCache.ContainsKey(currentInstr.dynamicNode)) outputRSI = RSIOutputCache[currentInstr.dynamicNode];

        
                            //Calculate the factor for the EMA
                            double factor = 1.0 / numberOfDays;

                            if (row == initialTest)
                            {
                                double[] closeValues = dataset.GetDoubleValues("\"Close\"", Enumerable.Range(0, (row + 1))).ToArray();
                                outputRSI = -1.0;
                                for (int i = 1; i <= row; i++)
                                {
                                    if (numberOfDays >= i)
                                    {
                                        if ((closeValues[i] - closeValues[i - 1]) > 0) positiveEMA = ((closeValues[i] - closeValues[i - 1]) + positiveEMA);
                                        else negativeEMA = Math.Abs(closeValues[i] - closeValues[i - 1]) + negativeEMA;
                                        if (numberOfDays == i)
                                        {
                                            positiveEMA = positiveEMA / numberOfDays;
                                            negativeEMA = negativeEMA / numberOfDays;
                                            yesterdayRSI = 100 - (100 / (1 + (positiveEMA / negativeEMA)));
                                        }
                                    }
                                    else
                                    {
                                        if ((closeValues[i] - closeValues[i - 1]) > 0)
                                        {
                                            positiveEMA = (closeValues[i] - closeValues[i - 1]) * factor + (1 - factor) * positiveEMA;
                                            negativeEMA = 0 * factor + (1 - factor) * negativeEMA;
                                        }
                                        else
                                        {
                                            positiveEMA = 0 * factor + (1 - factor) * positiveEMA;
                                            negativeEMA = Math.Abs(closeValues[i] - closeValues[i - 1]) * factor + (1 - factor) * negativeEMA;
                                        }

                                        todayRSI = 100 - (100 / (1 + (positiveEMA / negativeEMA)));

                                        if ((yesterdayRSI < downThreshold) && (todayRSI > downThreshold)) outputRSI = 1.0;
                                        else if ((yesterdayRSI > upThreshold) && (todayRSI < upThreshold)) outputRSI = -1.0;
                                        yesterdayRSI = todayRSI;
                                    }
                                }
                            }
                            else
                            {
                                string todayCloseString = dataset.GetValue(row, 2);
                                string yesterdayCloseString = dataset.GetValue((row - 1), 2);
                                double todayClose = Convert.ToDouble(todayCloseString);
                                double yesterdayClose = Convert.ToDouble(yesterdayCloseString);

                                //Calculating EMA
                                if ((todayClose - yesterdayClose) > 0)
                                {
                                    positiveEMA = (todayClose - yesterdayClose) * factor + (1 - factor) * positiveEMA;
                                    negativeEMA = 0 * factor + (1 - factor) * negativeEMA;
                                }
                                else
                                {
                                    positiveEMA = 0 * factor + (1 - factor) * positiveEMA;
                                    negativeEMA = Math.Abs(todayClose - yesterdayClose) * factor + (1 - factor) * negativeEMA;
                                }
                                todayRSI = 100 - (100 / (1 + (positiveEMA / negativeEMA)));
                                if ((yesterdayRSI < downThreshold) && (todayRSI > downThreshold)) outputRSI = 1.0;
                                else if ((yesterdayRSI > upThreshold) && (todayRSI < upThreshold)) outputRSI = -1.0;
                            }

                            //Save positive and negative EMA for the next iteration
                            RSIPositiveCache[currentInstr.dynamicNode] = positiveEMA;
                            RSINegativeCache[currentInstr.dynamicNode] = negativeEMA;
                            RSICache[currentInstr.dynamicNode] = todayRSI;
                            RSIOutputCache[currentInstr.dynamicNode] = outputRSI;
                           
                        
                        return outputRSI;
                    }

                default: throw new NotSupportedException();
            }
        }

        private byte MapSymbolToOpCode(ISymbolicExpressionTreeNode treeNode)
        {
            if (symbolToOpcode.ContainsKey(treeNode.Symbol.GetType()))
                return symbolToOpcode[treeNode.Symbol.GetType()];
            else
                throw new NotSupportedException("Symbol: " + treeNode.Symbol);
        }

        // skips a whole branch
        private void SkipInstructions(InterpreterState state)
        {
            int i = 1;
            while (i > 0)
            {
                i += state.NextInstruction().nArguments;
                i--;
            }
        }
        [StorableConstructor]
        private Interpreter(bool deserializing) : base(deserializing) { }
        private Interpreter(Interpreter original, Cloner cloner) : base(original, cloner) { }
        public override IDeepCloneable Clone(Cloner cloner)
        {
            return new Interpreter(this, cloner);
        }

        public Interpreter()
            : base("SymbolicDataAnalysisExpressionTreeInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.")
        {
            Parameters.Add(new ValueParameter<BoolValue>(CheckExpressionsWithIntervalArithmeticParameterName, "Switch that determines if the interpreter checks the validity of expressions with interval arithmetic before evaluating the expression.", new BoolValue(false)));
            Parameters.Add(new ValueParameter<IntValue>(EvaluatedSolutionsParameterName, "A counter for the total number of solutions the interpreter has evaluated", new IntValue(0)));
        }

        [StorableHook(HookType.AfterDeserialization)]
        private void AfterDeserialization()
        {
            if (!Parameters.ContainsKey(EvaluatedSolutionsParameterName))
                Parameters.Add(new ValueParameter<IntValue>(EvaluatedSolutionsParameterName, "A counter for the total number of solutions the interpreter has evaluated", new IntValue(0)));
        }
        public void setInitialTraining(int initialTraining)
        {
            this.initialTraining = initialTraining;
        }

        public void setInitialTest(int initialTest)
        {
            this.initialTest = initialTest;
        }

        public void clearVariables()
        {
            if (signalCache != null) signalCache.Clear();
            if (firstEMACache != null) firstEMACache.Clear();
            if (secondEMACache != null) secondEMACache.Clear();
            if (RSIPositiveCache != null) RSIPositiveCache.Clear();
            if (RSINegativeCache != null) RSINegativeCache.Clear();
            if (RSICache != null) RSICache.Clear();
            if (RSIOutputCache != null) RSIOutputCache.Clear();
        }

        //Take the symbolic expression values of the tree
        public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, IEnumerable<int> rows)
        {
            if (CheckExpressionsWithIntervalArithmetic.Value)
                throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
            EvaluatedSolutions.Value++; // increment the evaluated solutions counter
            var compiler = new SymbolicExpressionTreeCompiler();
            Instruction[] code = compiler.Compile(tree, MapSymbolToOpCode);//Take the type of symbol
            int necessaryArgStackSize = 0;
            for (int i = 0; i < code.Length; i++)
            {
                Instruction instr = code[i];
                if (instr.opCode == OpCodes.Variable)
                {
                    var variableTreeNode = instr.dynamicNode as VariableTreeNode;
                    instr.iArg0 = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
                    code[i] = instr;
                }
            }

            if (signalCache == null) signalCache = new Dictionary<ISymbolicExpressionTreeNode, double>();
            if (firstEMACache == null) firstEMACache = new Dictionary<ISymbolicExpressionTreeNode, double>();
            if (secondEMACache == null) secondEMACache = new Dictionary<ISymbolicExpressionTreeNode, double>();
            if (RSIPositiveCache == null) RSIPositiveCache = new Dictionary<ISymbolicExpressionTreeNode, double>();
            if (RSINegativeCache == null) RSINegativeCache = new Dictionary<ISymbolicExpressionTreeNode, double>();
            if (RSICache == null) RSICache = new Dictionary<ISymbolicExpressionTreeNode, double>();
            if (RSIOutputCache == null) RSIOutputCache = new Dictionary<ISymbolicExpressionTreeNode, double>();

            var state = new InterpreterState(code, necessaryArgStackSize);
            //Evaluate each row of the datase
            foreach (var rowEnum in rows)
            {
                int row = rowEnum;
                state.Reset();
                if (row < initialTest) yield return -1;
                else yield return Evaluate(dataset, ref row, state);
            }
        }
    }

}