diff --git a/API.py b/API.py
new file mode 100644
index 00000000..47a12fe1
--- /dev/null
+++ b/API.py
@@ -0,0 +1,39 @@
+import requests
+import json
+import time
+import os
+
+
+def cls(): os.system('cls' if os.name == 'nt' else 'clear')
+
+
+def calculateDiff(sell_price, buy_price):
+    if sell_price == None or buy_price == None:
+        return -1
+    return  (float(sell_price) - float(buy_price)) / float(buy_price)*100
+
+
+def loadFromApi():
+    url = 'https://api.bitbay.net/rest/trading/ticker'
+    headers = {'content-type': 'application/json'}
+    response = requests.request("GET", url, headers=headers)
+    return json.loads(response.text)
+
+
+def currencyMonitor(markets):
+    while True:
+        response_dict = loadFromApi()
+        cls()
+        for market in markets:
+            diff = calculateDiff(response_dict['items'][market]['lowestAsk'], response_dict['items'][market]['highestBid'])
+            print("Market: {0:15} Bid: {1:15}\tAsk: {2:15}Difference: {3:.2f} %".
+                format(market, str(response_dict['items'][market]['highestBid']), str(response_dict['items'][market]['lowestAsk']), diff))
+        time.sleep(5)
+
+def main():
+    markets_list = ("BTC-PLN","BTC-USD","ETH-USD","LTC-USD","LSK-USD","BCC-USD","DASH-USD")
+    currencyMonitor(markets_list)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/API_trade_sim.py b/API_trade_sim.py
new file mode 100644
index 00000000..c15d0f38
--- /dev/null
+++ b/API_trade_sim.py
@@ -0,0 +1,280 @@
+import requests
+import json
+import time
+from datetime import datetime, timedelta
+import os
+import csv
+import sys
+import numpy as np
+
+markets = ("USD-BTC", "USD-ETH", "BTC-LTC", "USD-LTC")
+markets_bitfinex = list('t' + market[4:] + market[:3] for market in markets)
+markets_binance = list(market[4:] + market[:3] +'T' if market[:3] == 'USD' else market[4:] + market[:3] for market in markets)
+markets_bitbay = list((market[4:]+'-'+market[:3] for market in markets))
+
+MARKETS_NUM = 4
+bittrexFee = 0.0025
+bitbayFee = 0.0041
+binanceFee = 0.001
+bitfinexFee = 0.002
+
+# 0 - USD-BTC
+# 1 - USD-ETH
+# 2 - BTC-LTC
+# 3 - USD-LTC
+bittrex_data = np.zeros(shape=(4,2))
+binance_data = np.zeros(shape=(4,2))
+bitfinex_data = np.zeros(shape=(4,2))
+bitbay_data = np.zeros(shape=(4,2))
+
+exchanges_data = [bittrex_data, binance_data, bitfinex_data, bitbay_data]
+exchanges_labels = ['Bittrex', 'Binance', 'Bitfinex', 'Bitbay']
+
+def cls(): os.system('cls' if os.name == 'nt' else 'clear')
+
+
+def calculateDiff(sell_price, buy_price):
+    if sell_price == None or buy_price == None:
+        return -1
+    return  (float(sell_price) - float(buy_price)) / float(buy_price)*100
+
+
+def loadFromApiBittrex(market, market_number):
+    url = 'https://api.bittrex.com/api/v1.1/public/getticker?market='+market
+    response = requests.get(url)
+    data = response.json()
+    bittrex_data[market_number][0] = data['result']['Bid']
+    bittrex_data[market_number][1] = data['result']['Ask']
+    return data['result']['Bid'], data['result']['Ask']
+
+
+def printDataBittrex(markets):
+    data = []
+    market_number = 0
+    for market in markets:
+        data.append(loadFromApiBittrex(market, market_number))
+        market_number+=1
+    output = "BITTREX\n"
+    for i in range(len(markets)):
+        diff = calculateDiff(data[i][1], data[i][0])
+        output += ("Market: {0:15} Bid: {1:15}\tAsk: {2:15}Difference: {3:.2f} %\n".
+            format(markets[i], str(data[i][0]), str(data[i][1]), diff))
+    return output
+    
+
+def loadFromApiBinance(market, market_number):
+    response = requests.get(f'https://api.binance.com/api/v3/depth?symbol={market}&limit=5')
+    data = response.json()
+    binance_data[market_number][0] = data['bids'][0][0]
+    binance_data[market_number][1] = data['asks'][0][0]
+    return data['bids'][0][0], data['asks'][0][0]
+
+
+def printDataBinance(markets):
+    data = []
+    market_number = 0
+    for market in markets_binance:
+        data.append(loadFromApiBinance(market, market_number))
+        market_number+=1
+    output = "BINANCE\n"
+    for i in range(len(markets)):
+        diff = calculateDiff(data[i][1], data[i][0])
+        output += ("Market: {0:15} Bid: {1:15}\tAsk: {2:15}Difference: {3:.2f} %\n".
+            format(markets[i], str(data[i][0]), str(data[i][1]), diff))
+    return output
+
+
+def loadFromApiBitfinex(market, market_number):
+    response = requests.get(f'https://api-pub.bitfinex.com/v2/book/{market}/P0?len=1')
+    data = response.json()
+    bitfinex_data[market_number][0] = data[0][0]
+    bitfinex_data[market_number][1] = data[1][0]
+    return data[1][0], data[0][0]
+
+
+def printDataBitfinex(markets):
+    data = []
+    market_number = 0
+    for market in markets_bitfinex:
+        data.append(loadFromApiBitfinex(market, market_number))
+        market_number+=1
+    output = "BITFINEX\n"
+    for i in range(len(markets)):
+        diff = calculateDiff(data[i][0], data[i][1])
+        output += ("Market: {0:15} Bid: {1:15}\tAsk: {2:15}Difference: {3:.2f} %\n".
+            format(markets[i], str(data[i][1]), str(data[i][0]), diff))
+    return output
+
+
+def loadFromApiBitBay():
+    url = 'https://api.bitbay.net/rest/trading/ticker'
+    headers = {'content-type': 'application/json'}
+    response = requests.request("GET", url, headers=headers)
+    response_dict = json.loads(response.text)
+    for i in range(len(markets)):
+        bitbay_data[i][0] = response_dict['items'][markets_bitbay[i]]['highestBid']
+        bitbay_data[i][1] = response_dict['items'][markets_bitbay[i]]['lowestAsk']
+    return response_dict
+
+
+def printDataBitBay(markets):
+    response_dict = loadFromApiBitBay()
+    output = "BITBAY\n"
+    for i in range(len(markets)):
+            diff = calculateDiff(response_dict['items'][markets_bitbay[i]]['lowestAsk'], response_dict['items'][markets_bitbay[i]]['highestBid'])
+            output += ("Market: {0:15} Bid: {1:15}\tAsk: {2:15}Difference: {3:.2f} %\n".
+                format(markets[i], str(response_dict['items'][markets_bitbay[i]]['highestBid']), str(response_dict['items'][markets_bitbay[i]]['lowestAsk']), diff))
+    return output
+
+
+
+def trader(investedSum, taxesOn):
+    while True:
+        bestDeal = searchProfit(markets, investedSum, taxesOn)
+        print(bestDeal)
+        if bestDeal[-1] > investedSum: investedSum = bestDeal[-1]
+        print("Money sum = ", bestDeal[-1])
+
+
+def decreaseByFee(market, moneyAmount):
+    if market == 'Bittrex':
+        return moneyAmount - moneyAmount*bittrexFee
+    elif market == 'Binance':
+        return moneyAmount - moneyAmount*binanceFee
+    elif market == 'Bitfinex':
+        return moneyAmount - moneyAmount*bitfinexFee
+    elif market == 'Bitbay':
+        return moneyAmount - moneyAmount*bitbayFee
+    else:
+        print("Error")
+        return -1
+    
+
+
+def searchProfit(markets, moneyAmount, taxesOn = True, specifiedIndex = -1):
+    lowestAsk = [float('inf') for x in range(4)]
+    highestBid = [-1*float('inf') for x in range(4)]
+    askExchange = ['','','','']
+    bidExchange = ['','','','']
+    interval = timedelta(seconds=1)
+    startTime = datetime.now()
+    control_iter = 0
+
+    while datetime.now() - startTime < interval:
+        control_iter+=1
+        for i in range(4):
+            loadFromApiBinance(markets_binance[i], i)
+            loadFromApiBitfinex(markets_bitfinex[i], i)
+            loadFromApiBittrex(markets[i], i)
+        loadFromApiBitBay()
+        for market in range(len(markets)):
+            for exchange in range(len(markets)):
+                testAsk = exchanges_data[exchange][market][1].item()
+                if testAsk < lowestAsk[market]:
+                    lowestAsk[market] = testAsk
+                    askExchange[market] = exchanges_labels[exchange]
+
+    startTime = datetime.now()    
+    control_iter = 0        
+    while datetime.now() - startTime < interval:
+        control_iter+=1
+        for i in range(4):
+            loadFromApiBinance(markets_binance[i], i)
+            loadFromApiBitfinex(markets_bitfinex[i], i)
+            loadFromApiBittrex(markets[i], i)
+        loadFromApiBitBay()
+        for market in range(len(markets)):
+            for exchange in range(len(markets)):
+                testBid = exchanges_data[exchange][market][0].item()
+                if testBid > highestBid[market]:
+                    highestBid[market] = testBid
+                    bidExchange[market] = exchanges_labels[exchange]
+
+    print(highestBid, "\t", lowestAsk)
+    diff = [calculateDiff(highestBid[i], lowestAsk[i]) for i in range(4)]
+    moneyAmount = [moneyAmount for i in range(4)]
+    print("diff = ", diff)
+    if specifiedIndex == -1:
+        for i in range(4):
+            if diff[i] > 0.0: 
+                if markets[i] != "BTC-LTC":
+                    if taxesOn: moneyAmount[i] = decreaseByFee(askExchange[i], moneyAmount[i])
+                    currencyAmount = moneyAmount[i]/lowestAsk[i]
+                    if taxesOn: currencyAmount = decreaseByFee(bidExchange[i], currencyAmount)
+                    moneyAmount[i] = currencyAmount*highestBid[i]
+                else:
+                    moneyAmount[i] = moneyAmount[i]/(exchanges_data[1][0][1].item())
+                    if taxesOn: moneyAmount[i] = decreaseByFee(askExchange[i], moneyAmount[i])
+                    currencyAmount = moneyAmount[i]/lowestAsk[i]
+                    if taxesOn: currencyAmount = decreaseByFee(bidExchange[i], currencyAmount)
+                    moneyAmount[i] = currencyAmount*highestBid[i]*(exchanges_data[1][0][0].item())
+                print(markets[i],"  Buy: ", askExchange[i], " - ", lowestAsk[i], "\tSell: ", bidExchange[i], " - ", highestBid[i],"\tDifference: {0:.2f} %".format(diff[i]), "\t Cash: ", moneyAmount[i])
+                outputToCsv([markets[i], askExchange[i], lowestAsk[i], bidExchange[i], highestBid[i], diff[i], moneyAmount[i]])
+            else: 
+                print(markets[i], "  No profitable pairs")
+                outputToCsv([markets[i], '-', '-', '-', '-', '-', '-'])
+    else:
+        if markets[specifiedIndex] != "BTC-LTC":
+            if taxesOn: moneyAmount[specifiedIndex] = decreaseByFee(askExchange[specifiedIndex], moneyAmount[specifiedIndex])
+            currencyAmount = moneyAmount[specifiedIndex]/lowestAsk[specifiedIndex]
+            if taxesOn: currencyAmount = decreaseByFee(bidExchange[specifiedIndex], currencyAmount)
+            moneyAmount[specifiedIndex] = currencyAmount*highestBid[specifiedIndex]
+        else:
+            moneyAmount[specifiedIndex] = moneyAmount[specifiedIndex]/(exchanges_data[1][0][1].item())
+            if taxesOn: moneyAmount[specifiedIndex] = decreaseByFee(askExchange[specifiedIndex], moneyAmount[specifiedIndex])
+            currencyAmount = moneyAmount[specifiedIndex]/lowestAsk[specifiedIndex]
+            if taxesOn: currencyAmount = decreaseByFee(bidExchange[specifiedIndex], currencyAmount)
+            moneyAmount[specifiedIndex] = currencyAmount*highestBid[specifiedIndex]*(exchanges_data[1][0][0].item())
+        print(markets[specifiedIndex],"  Buy: ", askExchange[specifiedIndex], " - ", lowestAsk[specifiedIndex], "\tSell: ", bidExchange[specifiedIndex], " - ", highestBid[specifiedIndex],"\tDifference: {0:.2f} %".format(diff[specifiedIndex]), "\t Cash: ", moneyAmount[specifiedIndex])
+        outputToCsv([markets[specifiedIndex], askExchange[specifiedIndex], lowestAsk[specifiedIndex], bidExchange[specifiedIndex], highestBid[specifiedIndex], diff[specifiedIndex], moneyAmount[specifiedIndex]])
+        return (markets[specifiedIndex], askExchange[specifiedIndex], lowestAsk[specifiedIndex], bidExchange[specifiedIndex], highestBid[specifiedIndex], diff[specifiedIndex], moneyAmount[specifiedIndex])
+
+    record_iter = moneyAmount.index(max(moneyAmount))
+    return (markets[record_iter], askExchange[record_iter], lowestAsk[record_iter], bidExchange[record_iter], highestBid[record_iter], diff[record_iter], moneyAmount[record_iter])
+
+
+def outputToCsv(output):
+    f = open('C:/Users/ashig/Documents/Python Scripts/MSiD/profitOutput.csv', 'a+', newline='')
+    with f:
+        writer = csv.writer(f)
+        writer.writerow(output)
+    f.close()
+
+
+def learnFromFile():
+    markets_average = [0 for i in range(4)]
+    f =  open('C:/Users/ashig/Documents/Python Scripts/MSiD/profitOutput.csv', 'r', newline='')
+    counter = 0
+    with f:
+        reader = csv.reader(f)
+        for row in reader:
+            counter += 1
+            if row[-2] != '-': markets_average[counter%4] = row[-2]
+    counter /= 4
+    markets_average = [float(market) / counter for market in markets_average] 
+    return markets_average.index(max(markets_average))
+
+
+def currencyMonitor(markets):
+    investedSum = 1000
+    learntIndex = learnFromFile()
+    while True:
+        output = printDataBittrex(markets)
+        output += printDataBinance(markets)
+        output += printDataBitfinex(markets)
+        output += printDataBitBay(markets)
+        print(output)
+        trade = searchProfit(markets, investedSum, False, specifiedIndex = learntIndex)
+        if trade[-1] > investedSum: investedSum = trade[-1]
+        print("Money sum = ", trade[-1])
+        print("\n\n")
+        time.sleep(5)
+        cls()
+
+
+def main():
+    currencyMonitor(markets)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/exchange_sim/exchangeSimulator.py b/exchange_sim/exchangeSimulator.py
new file mode 100644
index 00000000..fa3e8be0
--- /dev/null
+++ b/exchange_sim/exchangeSimulator.py
@@ -0,0 +1,158 @@
+from datetime import datetime
+from datetime import timezone
+from datetime import timedelta
+from pprint import pprint
+from random import choices
+from random import randint
+import tensorflow
+from sklearn.preprocessing import MinMaxScaler
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, LSTM
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import requests
+import time
+
+currency_pairs = ["btcusd", "ltcusd", "ethusd"] 
+scaler = MinMaxScaler(feature_range=(0, 1))
+
+
+def convert_date_to_utc(year, month, day, hour=0, minutes=0, seconds=0):
+    dt = datetime(year, month, day, hour, minutes, seconds)
+    timestamp = dt.replace(tzinfo=timezone.utc).timestamp()
+    return int(timestamp)
+
+
+def getData(currency_pair, start, end, step):
+    limit = (end - start) // step + 1
+    if limit > 1000:
+        limit = 1000
+    bitstamp_data = requests.get("https://www.bitstamp.net/api/v2/ohlc/{0}/".format(currency_pair),
+                                 params={'start': start, 'end': end, 'step': step, 'limit': limit})
+    if bitstamp_data.status_code == 200:
+        bitstamp_data_json = bitstamp_data.json()
+        return True, bitstamp_data_json['data']['ohlc']
+    else:
+        return False, None
+
+
+def prepareData(oldData):
+  data = pd.json_normalize(oldData)
+  previous_data_len = len(data)
+  for i in range(previous_data_len):
+      data.loc[i, 'timestamp'] = datetime.fromtimestamp(int(data.loc[i, 'timestamp']))
+  data.rename(columns={'timestamp':'date'}, inplace=True)
+
+  new_data = pd.DataFrame(index=range(0,len(data)), columns=['Date', 'Close'])
+  data_simulated = pd.DataFrame(index=range(0,len(data)), columns=['Date', 'Predicted_Close'])
+  for i in range(len(data)):
+      new_data['Date'][i] = data['date'][i]
+      new_data['Close'][i] = float(data['close'][i])
+      data_simulated['Date'][i] = data['date'][i] + timedelta(days=len(data))
+      data_simulated['Predicted_Close'][i] = 0.0 
+
+  new_data.index = new_data.Date
+  data_simulated.index = data_simulated.Date
+  new_data.drop('Date', axis=1, inplace=True)
+  data_simulated.drop('Date', axis=1, inplace=True)
+  dataset = new_data.values
+  dataset_simulated = data_simulated.values
+  return dataset, data_simulated, new_data
+
+
+def makeModel(dataset, dataset_simulated):
+  x_train, y_train = [], []
+  train_index = int(len(dataset)/5*4)
+  train = dataset
+  valid = dataset_simulated
+  scaled_data = scaler.fit_transform(dataset)
+
+  for i in range(60, len(train)):
+      x_train.append(scaled_data[i-60:i,0])
+      y_train.append(scaled_data[i,0])
+  x_train, y_train = np.array(x_train), np.array(y_train)
+  x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))
+
+  model = Sequential()
+  model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1],1)))
+  model.add(LSTM(units=50))
+  model.add(Dense(1))
+
+  model.compile(loss='mean_squared_error', optimizer='adam')
+  model.fit(x_train, y_train, epochs=5, batch_size=1, verbose=2)
+  return model, train, valid
+    
+
+def simulate(model, new_data, valid, data_simulated):
+  inputs = new_data[len(new_data) - len(valid) - 60:].values
+  inputs = inputs.reshape(-1,1)
+  inputs = scaler.transform(inputs)
+
+  X_test = []
+  X_test.append(inputs)
+  X_test = np.array(X_test)
+  X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))
+
+  simulation_output = []
+
+  for i in range(len(new_data)):
+    if i % randint(3,6) == 0:
+      predicted_price = differenceFunc(X_test)
+    else:
+      predicted_price = model.predict(X_test)
+    for j in range(1, 60):
+      X_test[0,j-1] = X_test[0,j]
+    X_test[0,59] = predicted_price
+    simulation_output.append(predicted_price)
+
+  train = new_data
+  valid = data_simulated
+
+  converted_output = []
+  for i in range(len(simulation_output)):
+    converted_output.append([simulation_output[i].item()])
+
+  converted_output = scaler.inverse_transform(converted_output)
+  valid['Predicted_Close'] = converted_output
+  return train, valid
+
+
+def differenceFunc(array):
+  diff = 0.0
+  for i in (30, len(array)):
+    diff += array[0,i]/array[0,i+1] - 1
+  diff /= (len(array) - 30)
+  diff_mults = [10,20,25,30,35,40,45,50,60]
+  mult_weights = [0.3, 0.2, 0.15, 0.1, 0.1, 0.05, 0.04, 0.04, 0.02]
+  if diff > 0.003:
+    return array[0,-1] - array[0,-1]*0.005*choices(diff_mults, mult_weights)
+  return array[0,-1] + array[0,-1]*0.005*choices(diff_mults, mult_weights)
+
+
+def plotResults(x, y, currency_index):
+  plt.figure(figsize=(14,10))
+  plt.plot(x['Close'])    #train
+  plt.plot(y['Predicted_Close'])  #valid
+  plt.title(currency_pairs[currency_index])
+  plt.gcf().autofmt_xdate()
+  plt.show()
+
+
+def getParameters():
+  currency = int(input("Please, enter the number of currency [0 - BTC-USD, 1 - LTC-USD, 2 - ETH-USD]: "))
+  date1 = input("Please, enter -date from- in format YYYY-MM-DD: ")
+  date2 = input("Please, enter -date to- in format YYYY-MM-DD: ") 
+  return currency, convert_date_to_utc(int(date1[0:4]), int(date1[5:7]), int(date1[8:10])), convert_date_to_utc(int(date2[0:4]), int(date2[5:7]), int(date2[8:10]))
+
+def main():
+  currency_index, date_from, date_to = getParameters()
+  passed, oldData = getData(currency_pairs[currency_index], start=date_from, end=date_to, step=86400)
+  dataset, data_simulated, new_data = prepareData(oldData)
+  model, train, valid = makeModel(dataset, data_simulated)
+  train, valid = simulate(model, new_data, valid, data_simulated)
+  plotResults(train, valid, currency_index)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file