'''MODULE 1:''' import IndicatorsClasses import readingURL import urllib.request import json def analyzeHeader(symbol: str) -> None: '''accesses the readingURL module to connect a company to the given symbol. Passes this information in a list to get it correctly formatted.''' access = readingURL.read() company, shares = access.readURL(symbol) info = [str(symbol), str(company), str(shares)] print(symbol) print(company) print(shares) table_Header = [['Date', 'Open', 'High', 'Low', 'Close', 'Volume' , 'Indicator', 'Buy?', 'Sell?']] tabFormat(table_Header) def tabFormat(info: list) -> None: '''takes a list and correctly applies tab formatting.''' for eachList in range(len(info)): tempStr = "" for component in info[eachList][:-1]: tempStr += str(component) + "\t" tempStr += str(info[eachList][-1]) print(tempStr) if __name__ == "__main__": symbol = input().upper() while True: try: n = int(input()) if n < 0 or n > 999: print("Invalid trading days. Please try again.") n = int(input()) else: break except ValueError: print("Invalid input. Please submit an integer.") combination = input() if "TR" in combination: analyzeHeader(symbol) callClass = IndicatorsClasses.calculateRange(combination) listDetails = callClass.calculate(symbol, n) tabFormat(listDetails) elif "MP" in combination or "MV" in combination: analyzeHeader(symbol) callClass = IndicatorsClasses.calculateAverage(combination) if "MP" in combination: listDetails = callClass.calculate(symbol, n) else: listDetails = callClass.calculateVolume(symbol, n) tabFormat(listDetails) elif "DP" in combination or "DV" in combination: analyzeHeader(symbol) callClass = IndicatorsClasses.calculateDirectional(combination) if "DP" in combination: listDetails = callClass.calculate(symbol, n) else: listDetails = callClass.calculateVolume(symbol, n) tabFormat(listDetails) else: print("Input Error (Line 3): Must specify a legal indicator and signal strategy on this line") print("Data provided for free by IEX") print("View IEX's Terms of Use") print("https://iextrading.com/api-exhibit-a/") '''MODULE 2:''' import urllib.request import json class read: def readURL(self, symbol: str) -> (str, int): '''looks through the list of available companies participating in the stock exchange by matching their symbol to the uses''' response = urllib.request.urlopen("https://api.iextrading.com/1.0/stock/market/previous") data = response.read() response.close() text = json.loads(data.decode(encoding = 'utf-8')) symbolsList = [] for entity in text: symbolsList.append(entity) if symbol in symbolsList: company = self.readSymbols(symbol) outShares = self.analyzeShares(symbol) return company, outShares else: print("Not a valid symbol.") def readSymbols(self, symbol: str) -> str: '''matches the symbol to a company name''' postOpening = urllib.request.urlopen("https://api.iextrading.com/1.0/ref-data/symbols") valueData = postOpening.read() postOpening.close() matchList = json.loads(valueData.decode(encoding = 'utf-8')) for eachDict in matchList: for key, value in eachDict.items(): if symbol == value: name = eachDict["name"] return name def analyzeShares(self, symbol: str) -> int: '''replaces the URL's query with the search symbol and analyzes the data from this URL''' newURL = "https://api.iextrading.com/1.0/stock/" + symbol + "/stats" openThis = urllib.request.urlopen(newURL) readInfo = openThis.read() openThis.close() compValues = json.loads(readInfo.decode(encoding = 'utf-8')) for component in compValues: outstanding = compValues["sharesOutstanding"] return outstanding def additionalInfo(self, symbol: str, backNDays: int) -> [[]]: '''finds information for the "date, opening price, highest price, lowest price, closing price, and volume''' newURL = "https://api.iextrading.com/1.0/stock/" + symbol + "/chart/6m" response = urllib.request.urlopen(newURL) read = response.read() response.close() data = json.loads(read.decode(encoding = 'utf-8')) infoNestList = [] for component in data[-backNDays:]: date = component["date"] openP = ('%.4f' %(component["open"])) high = ('%.4f' %(component["high"])) low = ('%.4f' %(component["low"])) closeP = ('%.4f' %(component["close"])) volume = ('%d' %(component["volume"])) infoNestList.append([date, openP, high, low, closeP, volume]) return infoNestList '''MODULE 3:''' class true_range: '''after called on from calcRange in "IndicatorsClass" module, iterates through nested list containing TR information, and determines if should buy (TR/ previous closing value> buyThreshold) or should sell (TR/ previous closing value < sellThreshold), or neither.''' def signal(self, nestDetails: [[]], buyThreshold: str, sellThreshold: str, calcN: int) -> [[]]: strategy = [] nestDetails[0].append("") nestDetails[0].append("") buyList = [] sellList = [] for eachDay in range(1, len(nestDetails)): prevClosePrice = nestDetails[eachDay-1][4] trueRange = nestDetails[eachDay][-1] if ">" in buyThreshold: if float(trueRange) <= float(buyThreshold[1:]): nestDetails[eachDay].append("") else: nestDetails[eachDay].append("BUY") else: if float(trueRange) < float(buyThreshold[1:]): nestDetails[eachDay].append("BUY") else: nestDetails[eachDay].append("") if ">" in sellThreshold: if float(trueRange) >= float(sellThreshold[1:]): nestDetails[eachDay].append("SELL") else: nestDetails[eachDay].append("") else: if float(trueRange) < float(sellThreshold[1:]): nestDetails[eachDay].append("SELL") else: nestDetails[eachDay].append("") return nestDetails class simple_moving: '''after called on from calcAvg in "IndicatorsClass" module, iterates through nested list containing SMA information, and determines if should buy (if prev day's closing price < its SMA AND opposite for today) or should sell (if prev day's closing price > its SMA AND opposite for today), or neither.''' def signal(self, nestDetails: [[]], buyThreshold: str, sellThreshold: str, calcN: int) -> [[]]: nestDetails[0].append("") nestDetails[0].append("") for eachDay in range(1,len(nestDetails)): SMA = nestDetails[eachDay][-1] closingPrice = nestDetails[eachDay][4] prevDaySMA = nestDetails[eachDay-1][6] prevDayClosing = nestDetails[eachDay-1][4] if closingPrice > SMA: if prevDayClosing < prevDaySMA: nestDetails[eachDay].append("BUY") else: nestDetails[eachDay].append('') else: nestDetails[eachDay].append('') if closingPrice < SMA: if prevDayClosing > prevDaySMA: nestDetails[eachDay].append("SELL") else: nestDetails[eachDay].append('') else: nestDetails[eachDay].append('') return nestDetails def simple_moving_volume(self, nestDetails: [[]], buyThreshold: str, sellThreshold: str, calcN: int) -> [[]]: '''after called on from calcAvg in "IndicatorsClass" module, iterates through nested list containing SMA information, and determines if should buy (if prev day's closing price < its SMA AND opposite for today) or should sell (if prev day's closing price > its SMA AND opposite for today), or neither.''' nestDetails[0].append("") nestDetails[0].append("") for eachDay in range(1,len(nestDetails)): SMA = nestDetails[eachDay][-1] volume = nestDetails[eachDay][5] prevDaySMA = nestDetails[eachDay-1][-1] prevDayVolume = nestDetails[eachDay-1][5] if volume > SMA: if prevDayVolume < prevDaySMA: nestDetails[eachDay].append("BUY") else: nestDetails[eachDay].append('') else: nestDetails[eachDay].append('') if volume < SMA: if prevDayVolume > prevDaySMA: nestDetails[eachDay].append("SELL") else: nestDetails[eachDay].append('') else: nestDetails[eachDay].append('') return nestDetails class directional: '''after called on from calcDirec in "IndicatorsClass" module, iterates through nested list containing indicator information, and determines if should buy (daily indicator > buy threshold) or should sell (daily indicator < sell threshold)''' def signal(self, nestDetails: [[]], buyThreshold: str, sellThreshold: str, calcN: int) -> [[]]: buyThreshold = float(buyThreshold) sellThreshold = float(sellThreshold) for eachDay in range(len(nestDetails)): todayIndicator = nestDetails[eachDay][6] prevIndicator = nestDetails[eachDay-1][6] if "+" in todayIndicator: todayIndicator = todayIndicator[1:] todayIndicator = float(todayIndicator) if "+" in prevIndicator: prevIndicator = prevIndicator[1:] prevIndicator = float(prevIndicator) if todayIndicator > buyThreshold: if prevIndicator <= buyThreshold: nestDetails[eachDay].append("BUY") else: nestDetails[eachDay].append("") else: nestDetails[eachDay].append("") if todayIndicator < sellThreshold: if prevIndicator >= sellThreshold: nestDetails[eachDay].append("SELL") else: nestDetails[eachDay].append("") else: nestDetails[eachDay].append("") return nestDetails '''MODULE 4:''' import readingURL import StrategiesClasses class calculateRange: def __init__(self, combination) -> [[]]: '''initializes the basic variables used throughout the calculate function.''' split = combination.split() self._indicator = split[0] self._buyThreshold = split[1] self._sellThreshold = split[2] def calculate(self, symbol, N) -> [[]]: '''calculates the indicator value (based on comparing closing prices) and appends to the end of each list in a nested list.''' accessAPI = readingURL.read() nestDetails = accessAPI.additionalInfo(symbol, N) TRList = [""] for eachDay in range(1,N): highest = float(nestDetails[eachDay][2]) lowest = float(nestDetails[eachDay][3]) prevClosing = float(nestDetails[eachDay-1][4]) if prevClosing > highest: highest = prevClosing if prevClosing < lowest: lowest = prevClosing TRange = "%.4f"%(100*((highest - lowest)/prevClosing)) TRList.append(TRange) for each in range(len(nestDetails)): nestDetails[each].append(TRList[each]) calcN = 0 buyOrSell = StrategiesClasses.true_range() nestDetails = buyOrSell.signal(nestDetails, self._buyThreshold, self._sellThreshold, calcN) return nestDetails class calculateAverage: def __init__(self, combination) -> [[]]: '''initializes the basic variables used throughout the calculate function.''' split = combination.split() self._indicator = split[0] self._calcN = int(split[1]) def calculate(self, symbol, N) -> [[]]: '''calculates the indicator value (based on comparing closing prices) and appends to the end of each list in a nested list.''' printN = N calcN = self._calcN accessAPI = readingURL.read() nestDetails = accessAPI.additionalInfo(symbol, printN) SMAList = [] closePriceList = [] for eachDay in range(len(nestDetails)): closePrice = float(nestDetails[eachDay][4]) closePriceList.append(closePrice) if eachDay < calcN-1: nestDetails[eachDay].append("") if eachDay == calcN-1: SMA = '%.4f'%(sum(closePriceList)/calcN) SMAList.append(SMA) elif eachDay > calcN-1: tot = (sum(closePriceList[eachDay-calcN:eachDay])) SMA = "%.4f"%(float(tot)/float(calcN)) SMAList.append(SMA) nestDetails[eachDay-1].append(SMA) if printN >= calcN: lastSMA = "%.4f" %(sum(closePriceList[-calcN:])/calcN) nestDetails[printN-1].append(lastSMA) self._buyThreshold = 0 self._sellThreshold = 0 buyOrSell = StrategiesClasses.simple_moving() nestDetails = buyOrSell.signal(nestDetails, self._buyThreshold, self._sellThreshold, calcN) return nestDetails def calculateVolume(self, symbol, N) -> [[]]: '''calculates the indicator value (based on comparing volumes) and appends to the end of each list in a nested list.''' printN = N calcN = self._calcN accessAPI = readingURL.read() nestDetails = accessAPI.additionalInfo(symbol, printN) SMAList = [] volumeList = [] for eachDay in range(len(nestDetails)): volume = float(nestDetails[eachDay][5]) volumeList.append(volume) if eachDay < calcN-1: nestDetails[eachDay].append("") if eachDay == calcN-1: SMA = '%.4f'%(sum(volumeList)/calcN) SMAList.append(SMA) elif eachDay > calcN-1: total = (sum(volumeList[eachDay-calcN:eachDay])) SMA = "%.4f"%(float(total)/float(calcN)) SMAList.append(SMA) nestDetails[eachDay-1].append(SMA) if printN >= calcN: lastSMA = "%.4f" %(sum(volumeList[-calcN:])/calcN) nestDetails[printN-1].append(lastSMA) self._buyThreshold = 0 self._sellThreshold = 0 buyOrSell = StrategiesClasses.simple_moving() nestDetails = buyOrSell.simple_moving_volume(nestDetails, self._buyThreshold, self._sellThreshold, calcN) return nestDetails class calculateDirectional: def __init__(self, combination): split = combination.split() self._indicator = split[0] self._calcN = int(split[1]) self._buyThreshold = split[2] self._sellThreshold = split[3] def calculate(self, symbol, N) -> [[]]: '''calculates the indicator value (based on comparing closing prices) and appends to the end of each list in a nested list.''' calcN = self._calcN printN = N accessAPI = readingURL.read() nestDetails = accessAPI.additionalInfo(symbol, printN) permList = [] closing = [] for eachDay in range(printN): tempList = [] closing.append(nestDetails[eachDay][4]) for n in range(eachDay-calcN, eachDay+1): if n >= 0: tempList.append(closing[n]) indicator = 0 for loopDay in range(len(tempList)): if loopDay-1 >=0: if float(tempList[loopDay]) > float(tempList[loopDay-1]): indicator+=1 elif float(tempList[loopDay]) < float(tempList[loopDay-1]): indicator-=1 if indicator > 0: sign = "+"+str(indicator) permList.append(sign) elif indicator < 0: permList.append(str(indicator)) else: permList.append("0") for eachDay in range(len(permList)): nestDetails[eachDay].append(permList[eachDay]) calcN = 0 buyOrSell = StrategiesClasses.directional() nestDetails = buyOrSell.signal(nestDetails, self._buyThreshold, self._sellThreshold, calcN) return nestDetails def calculateVolume(self, symbol, N) -> [[]]: '''calculates the indicator value (based on comparing volumes) and appends to the end of each list in a nested list.''' calcN = self._calcN printN = N accessAPI = readingURL.read() nestDetails = accessAPI.additionalInfo(symbol, printN) permList = [] volume = [] for eachDay in range(printN): tempList = [] volume.append(nestDetails[eachDay][5]) for n in range(eachDay-calcN, eachDay+1): if n >= 0: tempList.append(volume[n]) indicator = 0 for loopDay in range(len(tempList)): if loopDay-1 >=0: if float(tempList[loopDay]) > float(tempList[loopDay-1]): indicator+=1 elif float(tempList[loopDay]) < float(tempList[loopDay-1]): indicator-=1 if indicator > 0: sign = "+"+str(indicator) permList.append(sign) elif indicator < 0: permList.append(str(indicator)) else: permList.append("0") for eachDay in range(len(permList)): nestDetails[eachDay].append(permList[eachDay]) calcN = 0 buyOrSell = StrategiesClasses.directional() nestDetails = buyOrSell.signal(nestDetails, self._buyThreshold, self._sellThreshold, calcN) return nestDetails