Discussing the article: "Neural networks made easy (Part 52): Research with optimism and distribution correction" - page 2

Dmitriy Gizlyk 2023.08.08 06:00 #11

Dmitriy Gizlyk #:

I noticed a typo in the code of the Study.mq5 Expert Advisor

The files in the article have been updated.

Viktor Kudriavtsev 2023.08.14 06:15 #12

Dmitry, why does this network open all trades with exactly 1 lot, when training on all tests, and does not try to change the lot? It does not try to set fractional lots and does not want to set more than 1 lot either. EURUSD instrument. Training parameters are the same as yours.

Errors, bugs, questions Who wants a strategy? OSMA EA - good

Dmitriy Gizlyk 2023.08.14 14:41 #13

Viktor Kudriavtsev #:
Dmitry, why does this network open all trades with exactly 1 lot, when training on all tests, and does not try to change the lot? It doesn't try to put fractional lots and doesn't want to put more than 1 lot either. EURUSD instrument. Training parameters are the same as yours.

On the last Actor layer we use sigmoid as an activation function, which limits the values in the range [0,1]. For TP and SL, we use a multiplier to adjust the values. The lot size is not adjusted. Therefore, 1 lot is the maximum possible value.

//--- layer 9
   if(!(descr = new CLayerDescription()))
      return false;
   descr.type = defNeuronSoftActorCritic;
   descr.count = NActions;
   descr.window_out = 32;
   descr.optimization = ADAM;
   descr.activation = SIGMOID;
   if(!actor.Add(descr))
     {
      delete descr;
      return false;
     }

Discussion of article "Neural A quick and free Rent a Signal service

Viktor Kudriavtsev 2023.08.14 16:43 #14

Dmitriy Gizlyk #:

In the last Actor layer, we use sigmoid as the activation function, which limits the values to the range [0,1]. For TP and SL we use a multiplier to adjust the values. The lot size is not adjusted. Therefore, 1 lot is the maximum possible value.

Understood, Thank you.

JimReaper 2023.08.15 20:34 #15

ENJOY <3

//+------------------------------------------------------------------+

//| Expert tick function |

//+------------------------------------------------------------------+

void OnTick()

{

//---

if(!IsNewBar())

return;

//---

int bars = CopyRates(Symb.Name(), TimeFrame, iTime(Symb.Name(), TimeFrame, 1), HistoryBars, Rates);

if(!ArraySetAsSeries(Rates, true))

return;

//---

RSI.Refresh();

CCI.Refresh();

ATR.Refresh();

MACD.Refresh();

Symb.Refresh();

Symb.RefreshRates();

//---

float atr = 0;

for(int b = 0; b < (int)HistoryBars; b++)

{

float open = (float)Rates[b].open;

float rsi = (float)RSI.Main(b);

float cci = (float)CCI.Main(b);

atr = (float)ATR.Main(b);

float macd = (float)MACD.Main(b);

float sign = (float)MACD.Signal(b);

if(rsi == EMPTY_VALUE || cci == EMPTY_VALUE || atr == EMPTY_VALUE || macd == EMPTY_VALUE || sign == EMPTY_VALUE)

continue;

//---

int shift = b * BarDescr;

sState.state[shift] = (float)(Rates[b].close - open);

sState.state[shift + 1] = (float)(Rates[b].high - open);

sState.state[shift + 2] = (float)(Rates[b].low - open);

sState.state[shift + 3] = (float)(Rates[b].tick_volume / 1000.0f);

sState.state[shift + 4] = rsi;

sState.state[shift + 5] = cci;

sState.state[shift + 6] = atr;

sState.state[shift + 7] = macd;

sState.state[shift + 8] = sign;

}

bState.AssignArray(sState.state);

//---

sState.account[0] = (float)AccountInfoDouble(ACCOUNT_BALANCE);

sState.account[1] = (float)AccountInfoDouble(ACCOUNT_EQUITY);

//---

double buy_value = 0, sell_value = 0, buy_profit = 0, sell_profit = 0;

double position_discount = 0;

double multiplyer = 1.0 / (60.0 * 60.0 * 10.0);

int total = PositionsTotal();

datetime current = TimeCurrent();

for(int i = 0; i < total; i++)

{

if(PositionGetSymbol(i) != Symb.Name())

continue;

double profit = PositionGetDouble(POSITION_PROFIT);

switch((int)PositionGetInteger(POSITION_TYPE))

{

case POSITION_TYPE_BUY:

buy_value += PositionGetDouble(POSITION_VOLUME);

buy_profit += profit;

break;

case POSITION_TYPE_SELL:

sell_value += PositionGetDouble(POSITION_VOLUME);

sell_profit += profit;

break;

}

position_discount += profit - (current - PositionGetInteger(POSITION_TIME)) * multiplyer * MathAbs(profit);

}

sState.account[2] = (float)buy_value;

sState.account[3] = (float)sell_value;

sState.account[4] = (float)buy_profit;

sState.account[5] = (float)sell_profit;

sState.account[6] = (float)position_discount;

sState.account[7] = (float)Rates[0].time;

//---

bAccount.Clear();

bAccount.Add((float)((sState.account[0] - PrevBalance) / PrevBalance));

bAccount.Add((float)(sState.account[1] / PrevBalance));

bAccount.Add((float)((sState.account[1] - PrevEquity) / PrevEquity));

bAccount.Add(sState.account[2]);

bAccount.Add(sState.account[3]);

bAccount.Add((float)(sState.account[4] / PrevBalance));

bAccount.Add((float)(sState.account[5] / PrevBalance));

bAccount.Add((float)(sState.account[6] / PrevBalance));

double x = (double)Rates[0].time / (double)(D'2024.01.01.01' - D'2023.01.01');

bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));

x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_MN1);

bAccount.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));

x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_W1);

bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));

x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_D1);

bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));

//---

if (bAccount.GetIndex() >= 0 && !bAccount.BufferWrite())

return;

//---

if (!Actor.feedForward(GetPointer(bState), 1, false, GetPointer(bAccount)))

return;

//---

PrevBalance = sState.account[0];

PrevEquity = sState.account[1];

//---

vector<float> temp;

Actor.getResults(temp);

float delta = MathAbs(ActorResult - temp).Sum();

ActorResult = temp;

//---

double min_lot = Symb.LotsMin();

double step_lot = Symb.LotsStep();

double stops = MathMax(Symb.StopsLevel(), 1) * Symb.Point();

if (temp[0] >= temp[3])

{

temp[0] -= temp[3];

temp[3] = 0;

}

else

{

temp[3] -= temp[0];

temp[0] = 0;

}

//--- buy control

if (temp[0] < min_lot || (temp[1] * MaxTP * Symb.Point())) <= stops || (temp[2] * MaxSL * Symb.Point()) <= stops)

{

if (buy_value > 0)

CloseByDirection(POSITION_TYPE_BUY);

}

else

{

buy_lot = min_lot + MathRound((double)(temp[0] - min_lot) / step_lot) * step_lot;

buy_tp = NormalizeDouble(Symb.Ask() + temp[1] * MaxTP * Symb.Point(), Symb.Digits());

double buy_sl = NormalizeDouble(Symb.Ask() - temp[2] * MaxSL * Symb.Point(), Symb.Digits());

if (buy_value > 0)

TrailPosition(POSITION_TYPE_BUY, buy_sl, buy_tp);

if (buy_value != buy_lot)

{

if (buy_value > buy_lot)

ClosePartial(POSITION_TYPE_BUY, buy_value - buy_lot);

else

Trade.Buy(buy_lot - buy_value, Symb.Name(), Symb.Ask(), buy_sl, buy_tp);

}

//--- sell control

if (temp[3] < min_lot || (temp[4] * MaxTP * Symb.Point())) <= stops || (temp[5] * MaxSL * Symb.Point()) <= stops)

{

if (sell_value > 0)

CloseByDirection(POSITION_TYPE_SELL);

}

else

{

double sell_lot = min_lot + MathRound((double)(temp[3] - min_lot) / step_lot) * step_lot;

double sell_tp = NormalizeDouble(Symb.Bid() - temp[4] * MaxTP * Symb.Point(), Symb.Digits());

double sell_sl = NormalizeDouble(Symb.Bid() + temp[5] * MaxSL * Symb.Point(), Symb.Digits());

if (sell_value > 0)

TrailPosition(POSITION_TYPE_SELL, sell_sl, sell_tp);

if (sell_value != sell_lot)

{

if (sell_value > sell_lot)

ClosePartial(POSITION_TYPE_SELL, sell_value - sell_lot);

else

Trade.Sell(sell_lot - sell_value, Symb.Name(), Symb.Bid(), sell_sl, sell_tp);

}

// Calculate initial rewards

float iRewards = bAccount[0];

vector<float> log_prob;

Actor.GetLogProbs(log_prob);

// Normalize ATR to the range [0, 1]

float minATR = -100.0; // Adjust these values based on your data

float maxATR = 100.0;

float norm_atr = (atr - minATR) / (maxATR - minATR);

// Define weights for normalisation

float minWeight = 0.0;

float maxWeight = 1.0;

// Normalize initial rewards and normalized ATR based on weights

float norm_iRewards = (iRewards - minWeight) / (maxWeight - minWeight);

float norm_norm_atr = (norm_atr - minWeight) / (maxWeight - minWeight);

// Calculate penalty for no trades

double penalty = (buy_value + sell_value) == 0 ? (norm_norm_atr + atr / (PrevBalance + LogProbMultiplier)) : 0.0;

// Update rewards and log probabilities

for (ulong i = 0; i < temp.Size(); i++)

{

sState.action[i] = temp[i];

sState.log_prob[i] = log_prob[i];

}

// Calculate increment based on log_prob_sum

float iRewards_increment = MathLog((float)PrevBalance);

// Adjust rewards based on initial norm_iRewards

if (norm_iRewards != 0)

{

norm_iRewards += norm_norm_atr + iRewards_increment;

}

// Apply Z-score normalisation to norm_iRewards

float meanRewards = -10.0; // Calculate the mean of rewards over time

float stdRewards = 10.0; // Calculate the standard deviation of rewards over time

float normalized_iRewards = (norm_iRewards - meanRewards) / stdRewards;

// Apply power transformation to enhance data distribution (e.g., Box-Cox transform)

float power = 0.5; // Adjust the power parameter as needed

float transformed_reward = (pow(1 + normalised_iRewards, power) - 1) / power;

// Apply exponential transformation to enhance data distribution

float ZReward = (transformed_reward - (LogProbMultiplier)) / (5 - (LogProbMultiplier));

float Mreward = MathLog10(ZReward + 1);

float reward = (Mreward - (-1.0f)) / (0.4f - (-1.0f));

Apply square root transformation

Calculate the mapped values back to the range [1, 100]

float SRQTreward = sqrt(reward);

Print the rewards and other information

Print("Buy Value: ", buy_value);

Print("Sell Value: ", sell_value);

Print("Temperature: ", temp);

Print("Temperature Size: ", temp. Size());

Print("iRewards: ", iRewards);

Print("Normalised ATR: ", norm_norm_atr);

Print("Normalized iRewards: ", normalized_iRewards);

Print("Transformed Reward: ", transformed_reward);

Print("Trajectory Reward: ", ZReward);

Print("MATHLOG: ", Mreward);

Print("Scaled Reward: ", reward);

Print("SRQT Reward: ", SRQTreward);

Add state and normalised rewards to the trajectory

if (! Base.Add(sState, reward))

{

ExpertRemove(); Handle the case when adding to Base fails

}

//+------------------------------------------------------------------+

//| |

//+------------------------------------------------------------------+

bool CreateDescriptions(CArrayObj *actor, CArrayObj *critic)

{

//---

CLayerDescription *descr;

//---

if(!actor)

{

actor = new CArrayObj();

if(!actor)

return false;

}

if(!critic)

{

critic = new CArrayObj();

if(!critic)

return false;

}

//--- Actor

actor.Clear();

//--- Input layer

if(!(descr = new CLayerDescription()))

return false;

descr.type = defNeuronBaseOCL;

int prev_count = descr.count = (HistoryBars * BarDescr);

descr.window = 0;

descr.activation = None;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 1

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConvOCL;

prev_count = descr.count = prev_count - 1;

descr.window = 7;

descr.step = 3;

descr.window_out = 8;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 2

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConvOCL;

prev_count = descr.count = prev_count - 1;

descr.window = 5;

descr.step = 2;

descr.window_out = 8;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 3

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConvOCL;

prev_count = descr.count = prev_count - 1;

descr.window = 3;

descr.step = 1;

descr.window_out = 8;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 4

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

descr.count = 1024;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 5

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

prev_count = descr.count = 512;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 6

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConvOCL;

prev_count = descr.count = prev_count - 1;

descr.window = 6;

descr.step = 2;

descr.window_out = 8;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 7

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConvOCL;

prev_count = descr.count = prev_count - 1;

descr.window = 4;

descr.step = 2;

descr.window_out = 8;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 8

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConvOCL;

prev_count = descr.count = prev_count - 1;

descr.window = 2;

descr.step = 1;

descr.window_out = 8;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 9

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConvOCL;

prev_count = descr.count = prev_count;

descr.window = 8;

descr.step = 8;

descr.window_out = 8;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 10

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBatchNormOCL;

descr.count = prev_count;

descr.batch = 1000;

descr.activation = None;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 11

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

descr.count = 1024;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 12

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

prev_count = descr.count = 512;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 13

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConcatenate;

descr.count = LatentCount;

descr.window = prev_count;

descr.step = AccountDescr;

descr.optimise = ADAM;

descr.activation = SIGMOID;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 14

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

descr.count = 1024;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 15

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

prev_count = descr.count = 1024;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 16

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

descr.count = prev_count;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- layer 17

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronSoftActorCritic;

descr.count = NActions;

descr.window_out = 32;

descr.optimise = ADAM;

descr.activation = SIGMOID;

if(!actor.Add(descr))

{

delete descr;

return false;

}

//--- Critic

critic.Clear();

//--- Input layer

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

prev_count = descr.count = LatentCount;

descr.window = 0;

descr.activation = None;

descr.optimisation = ADAM;

if(!critic.Add(descr))

{

delete descr;

return false;

}

//--- layer 1

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronConcatenate;

descr.count = 1024;

descr.window = prev_count;

descr.step = 6;

descr.optimise = ADAM;

descr.activation = LReLU;

if(!critic.Add(descr))

{

delete descr;

return false;

}

//--- layer 2

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

descr.count = 1024;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!critic.Add(descr))

{

delete descr;

return false;

}

//--- layer 3

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

descr.count = 1024;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!critic.Add(descr))

{

delete descr;

return false;

}

//--- layer 4

if(!(descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

descr.count = 1024;

descr.activation = LReLU;

descr.optimisation = ADAM;

if(!critic. Add(descr))

{

delete descr;

return false;

}

--- layer 5

if(!( descr = new CLayerDescription())))

return false;

descr.type = defNeuronBaseOCL;

descr.count = 1;

descr.optimise = ADAM;

descr.activation = None;

if(!critic. Add(descr))

{

delete descr;

return false;

}

//---

return true;

}

Discussing the article: "Neural Discussing the article: "Neural Discussing the article: "Neural

JimReaper 2023.08.17 03:05 #16

Thanks

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