Fortune/callSpread.py at main · Austin-Src/Fortune · GitHub

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import ameritrade as am
import requests

# Calculates where the Covered Call Spread stops being profitable
def getZeroProfit(stockPrice, sellStrike, buyStrike, sellPrice, buyPrice):
	hitZero = 0

	# Check that return is positive
	data = calculateProfits(0, stockPrice,
		sellStrike, 0, buyStrike, 0, sellPrice, buyPrice, 0)

	if data is not None:
		if data[5]>= 0:
			spreadCost = data[5]
			hitZero = ((stockPrice - (buyStrike + spreadCost)) / stockPrice) * 100
	return hitZero

# Identifies profit return for the Covered Call Spread
def calculateProfits(change, stockPrice, sellStrike, sellInterest, buyStrike,
	buyInterest, sellPrice, buyPrice, hitsZero):

	stock_Price = stockPrice * change
	buyValue = float(stock_Price) - float(buyStrike)
	sellValue = sellStrike - stock_Price
	if sellStrike >= stock_Price:
		sellValue = 0.00

	spreadValue = buyValue + sellValue
	spreadCost = buyPrice - sellPrice

	if buyPrice == 0:
		spreadCost = 0.0
	if sellPrice == 0:
		spreadCost = 0.0

	spreadProfit = spreadValue - spreadCost
	if spreadCost == 0:
		percentReturn = 0.0
	else:
		percentReturn = spreadProfit / spreadCost

	return [stockPrice, str(buyStrike) + "  :  " + str(buyInterest), str(sellStrike) + "  :  " + str(sellInterest),
		 buyPrice, sellPrice, spreadCost, spreadProfit, percentReturn * 100, hitsZero]

# Joins the Options Spread profit and durability into a final table
def getProfits(stock_Price, sell_Strike, sell_interest, buy_Strike, buy_interest, sell_Price, buy_Price):
	outputs = []

	hitsZero = getZeroProfit(stock_Price, sell_Strike, buy_Strike,
		sell_Price, buy_Price)
	outputs.append(calculateProfits(1.1, stock_Price, sell_Strike, sell_interest,
		buy_Strike, buy_interest, sell_Price, buy_Price, hitsZero))
	return outputs

# Combines and calls all functions for Covered Call Spreads
def callSpread(stock, expiration, protection, spreadReturn, spreadCost, openInterest, priceSpread):

	data = am.callAPI('CALL', stock)
	expDate = 'callExpDateMap'
	selections = am.getDates(data, expDate)
	x = expiration

	# Array used to store all final outputs
	outputs = []

	# Check that the Call has an Options Chain for Selected Date
	try:
		y = data[expDate][x]
	except:
		return

	# Iterate all strikes
	for y in data[expDate][x]:
		bid = data[expDate][x][y][0]['bid']
		ask = data[expDate][x][y][0]['ask']
		yPrice = (bid+ask)/2
		underlyingPrice = data['underlyingPrice']
		strikePrice = data[expDate][x][y][0]['strikePrice']


		# Second loop for comparing all strikes against eachother
		for s in data[expDate][x]:
			if s >= y:		# Only compare when SellStrike is higher for Debit spreads
				continue
			if data[expDate][x][s][0]['inTheMoney'] == 'false':
				continue

			bid = data[expDate][x][s][0]['bid']
			ask = data[expDate][x][s][0]['ask']
			sPrice = (bid+ask) / 2

			#print(data[expDate][x][s][0]['description'])
			test = getProfits(data['underlyingPrice'],
				data[expDate][x][y][0]['strikePrice'],
				data[expDate][x][y][0]['openInterest'],
				data[expDate][x][s][0]['strikePrice'],
				data[expDate][x][s][0]['openInterest'],
				yPrice, sPrice)

			# Narrowing parameters
			minProtection = protection
			minPercentReturn = spreadReturn
			minSpreadCost = spreadCost
			naturalPrice = data[expDate][x][s][0]['ask'] - data[expDate][x][y][0]['bid']
			midPrice = test[0][5]

			# Refine program outputs
			if test[0][8] is not None:
				if float(test[0][8]) >= float(minProtection):
					if float(test[0][7]) >= float(minPercentReturn):
						if float(test[0][5]) >= float(minSpreadCost):
							if data[expDate][x][s][0]['openInterest'] >= int(openInterest):
								if data[expDate][x][y][0]['openInterest'] >= int(openInterest):
									if data[expDate][x][y][0]['nonStandard'] == False:
										if naturalPrice - midPrice <= float(priceSpread):
											# Add stock symbol to table rows
											test[0][0] = str(data[expDate][x][s][0]['description'].split(" ")[0]) + " @ " + str(round(test[0][0], 2))

											outputs.append(test)
	# If no results after refining, don't print
	if outputs == []:
		return

	# Flatten, Sort, and Tabulate final outputs
	flat_list = []
	for sublist in outputs:
    		for item in sublist:
        		flat_list.append(item)
	return am.Sort(flat_list)