def profit_analysis():
gross_profit = [r-o for r, o in zip(d.revenue, d.operating_expense)]
period_expense = []
for i in range(len(d.selling_expense)):
period_expense.append(
sum([d.selling_expense[i], d.management_expense[i], d.financing_expense[i]]))
print("二、利润表分析:")
print("1.基本信息:")
print(string + '营业收入:', [round(i, 2) for i in d.revenue])
print(string + '营业成本:', [round(i, 2) for i in d.operating_expense])
print(string + '营业成本增长率', f.percentageChange(d.operating_expense))
print(string + '营业利润', [round(no, 2) for no in d.operating_profit])
print(string + '营业利润变化', f.percentageChange(d.operating_profit))
print(string + '净利润', [round(no, 2) for no in d.net_profit])
print(string + '净利润变化', f.percentageChange(d.net_profit))
print(string + '销售费用', [round(no, 2) for no in d.selling_expense])
print(string + '管理费用', [round(no, 2) for no in d.management_expense])
print(string + '财务费用', [round(no, 2) for no in d.financing_expense])
print(string + '期间费用', [round(no, 2) for no in period_expense])
print(string + '期间费用变化', f.percentageChange(period_expense))
print(string + '三费占比', f.percentageOccupied(period_expense, d.revenue))
print("2. 利润结构分析数据:")
print(string + '营业利润占比利润总额', f.percentageOccupied(d.operating_profit, d.overall_profit))
print(string + '毛利', gross_profit)
print(string + '毛利率', f.ratio(gross_profit, d.revenue))
print(string + '毛利率变化', f.percentageChange(f.ratio(gross_profit, d.revenue)))
def cashflow_analysis():
print("三、现金流量表分析:")
print("1. 基本信息:")
print(string + '经营活动产生的现金流入小计', [round(i, 2) for i in d.operation_ttinflow])
print(string + '经营活动产生的现金流出小计', [round(i, 2) for i in d.operation_ttoutflow])
print(string + '经营活动产生的现金流量', [round(i, 2) for i in d.operation_cashflow])
print(string + '经营现金净流量增长率', f.percentageChange(d.operation_cashflow))
print(string + '投资活动产生的现金流入小计', [round(i, 2) for i in d.investment_ttinflow])
print(string + '投资活动产生的现金流出小计', [round(i, 2) for i in d.investment_ttoutflow])
print(string + '投资活动产生的现金流量', [round(i, 2) for i in d.investment_cashflow])
print(string + '投资活动产生的现金流量变化', f.percentageChange(d.investment_cashflow))
print(string + '筹资活动产生的现金流入小计', [round(i, 2) for i in d.financing_ttinflow])
print(string + '筹资活动产生的现金流出小计', [round(i, 2) for i in d.financing_ttoutflow])
print(string + '筹资活动产生的现金流量', [round(i, 2) for i in d.financing_cashflow])
print(string + '筹资活动产生的现金流量变化', f.percentageChange(d.financing_cashflow))
print("2. 企业销售利润、销售回款和创现能力（公司销售商品提供劳务收到的现金与购进劳务付出的现金比）:",
f.ratio(d.operation_inflow, d.operation_outflow))
print("3. 企业的主营业务与营销状况（销售商品提供劳务产生的现金流入于流入现金总额占比）:",
f.percentageOccupied(d.operation_inflow, d.operation_ttinflow))
print("4. 现金流入组成：", f.percentage_compostion([d.operation_ttinflow, d.investment_ttinflow,
d.financing_ttinflow]))
print("5. 现金流出组成：")
print("6. 净现金流量总量分析：")
print(string + "净现金流量总量 ", [round(i, 2) for i in d.total_cashflow])
print(string + "净现金流量总量变化率 ", f.percentageChange(d.total_cashflow))
print(string + "经营现金流量占比 ", f.percentageOccupied(d.operation_cashflow, d.total_cashflow))
print(string + "投资现金流量占比 ", f.percentageOccupied(d.investment_cashflow, d.total_cashflow))
print(string + "筹资先金流量占比 ", f.percentageOccupied(d.financing_cashflow, d.total_cashflow))
def ratio_analysis():
print("1. 短期偿债能力:")
print(string + '流动比率', f.ratio(d.current_asset, d.current_liability))
print(string + '速动比率', f.ratio(d.quick_asset, d.current_liability))
print("2. 长期负债能力:")
print(string + '资产负债率', f.percentageOccupied(d.total_liability, d.total_asset))
print(string + '股东权益比率', f.percentageOccupied(d.owner_equity, d.total_asset))
print(string + '权益乘数', f.percentageOccupied(d.total_asset, d.owner_equity))
print(string + '产权比率', f.percentageOccupied(d.total_liability, d.owner_equity))
print("3.发展能力:")
print(string + '净资产增长率', f.percentageChange(d.owner_equity))
print(string + '销售增长率', f.percentageChange(d.revenue))
print(string + '总资产增长率', f.percentageChange(d.total_asset))
print("4. 盈利能力分析数据:")
print(string + '销售净利率', f.percentageOccupied(d.net_profit, d.revenue))
print(string + '总资产收益率', f.percentageOccupied(d.net_profit, d.total_asset))
print(string + '净资产收益率', f.percentageOccupied(d.net_profit, d.owner_equity))
def optimization(weight_scheme):
numberofday = 0
quarter_list = []
# Initialize matrix full of zeros
resultMatrix = [[0 for x in range(len(ticker) + 5)]
for y in range(len(timestamp) + 1)]
# Change the first row of result matrix to show headers
firstrow = resultMatrix[0]
secondrow = resultMatrix[1]
for i0 in range(len(firstrow)):
if i0 == 0:
firstrow[i0] = ""
elif i0 < len(ticker) + 1:
firstrow[i0] = ticker[i0 - 1]
elif i0 == len(ticker) + 1:
firstrow[i0] = "Daily Return"
elif i0 == len(ticker) + 2:
firstrow[i0] = "Cumulative"
elif i0 == len(ticker) + 3:
firstrow[i0] = "Stock Count"
elif i0 == len(ticker) + 4:
firstrow[i0] = "Portfolio"
secondrow[i0] = initial_amount
# Change the first column of result matrix to show time index
for row in range(len(resultMatrix)):
if row == 0:
resultMatrix[row][0] = ""
else:
resultMatrix[row][0] = timestamp[row - 1]
resultMatrix = np.array(resultMatrix)
column_return = np.array(resultMatrix[:, -1][1:]) # Portfolio
column_return = np.asfarray(column_return, float)
column_return[0] = initial_amount
# Loop through time index
cash_amount = 0
for index in range(len(timestamp)):
bool_quarter = False
stockcount = 0
answer = 0 # search for index for weights
month = int(timestamp[index].split("/")[1])
year = int(timestamp[index].split("/")[2])
if month < 7:
quarter = "Q2"
else:
quarter = "Q4"
current = str(year) + quarter
# Q-2 quarter
if month < 4:
year -= 1
quarter = "Q2"
elif month < 7:
year -= 1
quarter = "Q4"
elif month < 10:
year -= 1
quarter = "Q4"
else:
quarter = "Q2"
date = str(year) + quarter
# Check if quarter is different from previous day's quarter
quarter_list.append(current)
if index == 0:
store_index.append([index, current])
else:
if quarter_list[index] != quarter_list[index - 1]:
store_index.append([index, current])
if (index
== 0) or (index != 0
and quarter_list[index] != quarter_list[index - 1]):
bool_quarter = True
# Search for index for weights
for index2 in range(len(weight_scheme)):
if date == weight_scheme[index2][0]:
answer = index2
# Loop through stocks
aggregate = 0
cash_hand = 100
for stock in range(len(ticker)):
gain = functions.percentageChange(
float(price[index - 1][stock + 1]),
float(price[index][stock + 1]))
if answer == 0:
stockweight = 0
else:
w = float(weight_scheme[answer][stock + 1])
stockweight = w / 100
if bool_quarter: # When quarter changes
if index == 0:
value = initial_amount * stockweight
else:
value = column_return[index - 1] * stockweight
cash_hand -= w
else: # When quarter does not change
value = float(
resultMatrix[index][stock + 1]) * (1 + gain / 100)
resultMatrix[index + 1][
stock +
1] = value # Fill in the money allocation instead of percentage change
aggregate += value
# Calculate number of stocks to be invested
if stockweight != 0 and float(price[index][stock + 1]) != 0:
stockcount += 1
# Calculate cash in hand when remaining weight is not zero
if bool_quarter and cash_hand < 100:
cash_amount = cash_hand * column_return[index - 1] / 100
if bool_quarter and cash_hand == 100:
cash_amount = 0
if aggregate == 0:
aggregate = column_return[index - 1]
if column_return[index - 1] == 0 or index == 0:
aggregate = initial_amount
aggregate += cash_amount
column_return[index] = aggregate
# Stock Count
resultMatrix[index + 1][len(ticker) + 3] = stockcount
numberofday += 1
resultMatrix[:, -1][1:] = column_return
daily_return = functions.percentagelist(column_return)
resultMatrix[:, -4][2:] = daily_return
cumulative = functions.cumulative_list(daily_return)
resultMatrix[:, -3][2:] = cumulative
portfolio_list = column_return
# Take the last index to get cumulative_return
cumulative_return = cumulative[-1] # Final cumulative return
list_cumulative.append(cumulative_return)
# Calculate related stats
sharpe_ratio = sharpe(daily_return)
list_sharpe.append(sharpe_ratio)
print(numberofday, "days;", round(cumulative_return, 4), "%, Sharpe = ",
round(sharpe_ratio, 4))
return column_return
#timing = end - start
#print(int(timing/60), " minutes and", round(timing-60*int(timing/60), 4), " seconds to plot graphs")
#print("\n")
########################################################
print("Semiannually")
# Calculate Quarterly Return (HK: semiannually)
quarter_matrix = [[0 for x in range(len(store_index))] for y in range(5)]
quarter_matrix[0][0] = "Semiannual Return"
quarter_matrix[1][0] = "Pure Market Timing"
quarter_matrix[2][0] = "Weight Scheme 1 + Market Timing"
quarter_matrix[3][0] = "Weight Scheme 2 + Market Timing"
quarter_matrix[4][0] = "Weight Optimized + Market Timing"
for i in range(len(store_index) - 1):
quarter_matrix[0][i + 1] = store_index[i][1]
quarter_matrix[1][i + 1] = functions.percentageChange(
portfolio_pure[store_index[i][0]],
portfolio_pure[store_index[i + 1][0]])
quarter_matrix[2][i + 1] = functions.percentageChange(
portfolio_weight1[store_index[i][0]],
portfolio_weight1[store_index[i + 1][0]])
quarter_matrix[3][i + 1] = functions.percentageChange(
portfolio_weight2[store_index[i][0]],
portfolio_weight2[store_index[i + 1][0]])
quarter_matrix[4][i + 1] = functions.percentageChange(
portfolio_weight_optimized[store_index[i][0]],
portfolio_weight_optimized[store_index[i + 1][0]])
# Benchmark Quarter Return: HSCI
quarter_list = []
store_index = []
quarter_benchmark = []
for index in range(len(date_index)):
def backtest(weight_scheme, number):
numberofday = 0
quarter_list = []
# Initialize matrix full of zeros
resultMatrix = [[0 for x in range(len(ticker) + 5)]
for y in range(len(timestamp) + 1)]
# Change the first row of result matrix to show headers
firstrow = resultMatrix[0]
secondrow = resultMatrix[1]
for i0 in range(len(firstrow)):
if i0 == 0:
firstrow[i0] = ""
elif i0 < len(ticker) + 1:
firstrow[i0] = ticker[i0 - 1]
elif i0 == len(ticker) + 1:
firstrow[i0] = "Daily Return"
elif i0 == len(ticker) + 2:
firstrow[i0] = "Cumulative"
elif i0 == len(ticker) + 3:
firstrow[i0] = "Stock Count"
elif i0 == len(ticker) + 4:
firstrow[i0] = "Portfolio"
secondrow[i0] = initial_amount
# Change the first column of result matrix to show time index
for row in range(len(resultMatrix)):
if row == 0:
resultMatrix[row][0] = ""
else:
resultMatrix[row][0] = timestamp[row - 1]
resultMatrix = np.array(resultMatrix)
column_return = np.array(resultMatrix[:, -1][1:]) # Portfolio
column_return = np.asfarray(column_return, float)
column_return[0] = initial_amount
# Loop through time index
for index in range(len(timestamp)):
bool_quarter = False
stockcount = 0
answer = 0 # search for index for weights
month = int(timestamp[index].split("/")[1])
year = int(timestamp[index].split("/")[2])
if month < 7:
quarter = "Q2"
else:
quarter = "Q4"
current = str(year) + quarter
# Q-2 quarter
if month < 4:
year -= 1
quarter = "Q2"
elif month < 7:
year -= 1
quarter = "Q4"
elif month < 10:
year -= 1
quarter = "Q4"
else:
quarter = "Q2"
date = str(year) + quarter
# Check if quarter is different from previous day's quarter
quarter_list.append(current)
if number == 1:
if index == 0:
store_index.append([index, current])
else:
if quarter_list[index] != quarter_list[index - 1]:
store_index.append([index, current])
if (index
== 0) or (index != 0
and quarter_list[index] != quarter_list[index - 1]):
bool_quarter = True
# Search for index for weights
for index2 in range(len(weight_scheme)):
if date == weight_scheme[index2][0]:
answer = index2
# Loop through stocks
aggregate = 0
for stock in range(len(ticker)):
gain = functions.percentageChange(
float(price[index - 1][stock + 1]),
float(price[index][stock + 1]))
if answer == 0:
stockweight = 0
else:
stockweight = float(weight_scheme[answer][stock + 1]) / 100
if bool_quarter: # When quarter changes
if index == 0:
value = initial_amount * stockweight
else:
value = column_return[index - 1] * stockweight
else: # When quarter does not change
value = float(
resultMatrix[index][stock + 1]) * (1 + gain / 100)
resultMatrix[index + 1][
stock +
1] = value # Fill in the money allocation instead of percentage change
aggregate += value
# Calculate number of stocks to be invested
if stockweight != 0 and float(price[index][stock + 1]) != 0:
stockcount += 1
if aggregate == 0:
aggregate = column_return[index - 1]
if column_return[index - 1] == 0 or index == 0:
aggregate = initial_amount
column_return[index] = aggregate
# Stock Count
resultMatrix[index + 1][len(ticker) + 3] = stockcount
numberofday += 1
resultMatrix[:, -1][1:] = column_return
daily_return = functions.percentagelist(column_return)
resultMatrix[:, -4][2:] = daily_return
cumulative = functions.cumulative_list(daily_return)
resultMatrix[:, -3][2:] = cumulative
portfolio_list = column_return
# Take the last index to get cumulative_return
cumulative_return = cumulative[-1] # Final cumulative return
# Calculate related stats
head = "Weight Scheme " + str(number)
print(head, ":")
print(numberofday, "days;", round(cumulative_return, 4), "%")
print("\n")
print("Average Daily Return =", round(average(daily_return), 4), "%")
print("SD of Daily Return =", round(SD(daily_return), 4), "%")
annual_return = average(daily_return) * oneyear
print("Annualized Return =", round(annual_return, 4), "%")
annual_volatility = SD(daily_return) * (oneyear**(1 / 2))
print("Annualized Volatility =", round(annual_volatility, 4), "%")
sharpe_ratio = sharpe(daily_return)
print("Annual Sharpe Ratio: ", round(sharpe_ratio, 4))
print("Max Consecutive Loss = ", maxconsecutive(daily_return), "days")
print("Win Rate:", round(winrate(daily_return), 4), "%")
max_drawdown = functions.findMin(daily_return)
print("Max Drawdown:", round(max_drawdown, 4), "%")
print("\n")
stats_line = [
head, cumulative_return, annual_return, annual_volatility,
sharpe_ratio, max_drawdown
]
stats_matrix.append(stats_line)
if number == 2:
# Calculate stats for market index
daily_return_index = functions.percentagelist(price_index)
cumulative_index = functions.percentageChange(price_index[0],
price_index[-1])
print(market, "Index: ")
print("Cumulative =", round(cumulative_index, 4), "%")
print("Average Daily Return =", round(average(daily_return_index), 4),
"%")
print("SD of Daily Return =", round(SD(daily_return_index), 4), "%")
annual_return = average(daily_return_index) * oneyear
print("Annualized Return =", round(annual_return, 4), "%")
annual_volatility = SD(daily_return_index) * (oneyear**(1 / 2))
print("Annualized Volatility =", round(annual_volatility, 4), "%")
sharpe_ratio = sharpe(daily_return_index)
print("Annual Sharpe Ratio: ", round(sharpe_ratio, 4))
print("Max Consecutive Loss = ", maxconsecutive(daily_return_index),
"days")
print("Win Rate:", round(winrate(daily_return_index), 4), "%")
max_drawdown = functions.findMin(daily_return)
print("Max Drawdown:", round(max_drawdown, 4), "%")
stats_line = [
market, cumulative_index, annual_return, annual_volatility,
sharpe_ratio, max_drawdown
]
stats_matrix.append(stats_line)
# Find correct date to match dates for market index and our portfolio
correctdate = []
correctdate2 = []
for i in range(len(timestamp)):
for j in range(len(date_index)):
if date_index[j] == timestamp[i]:
correctdate.append(i)
correctdate2.append(j)
# Prepare for a graph
x_axis = date_index
y = [] # Strategy Portfolio
y_index = [] # Market Index Portfolio
# Normalize to initial amount and fill up values for y-axis by searching for correct dates
relative_portfolio = functions.relative(portfolio_list)
for i in range(len(correctdate)):
correctindex = correctdate[i] - 1
y.append(relative_portfolio[correctindex])
for j in range(len(correctdate2)):
correctindex = correctdate2[j]
y_index.append(price_index[correctindex])
y_index = functions.relative(y_index)
# Plot a graph
fig = plt.figure() # Create a figure
ax = plt.axes() # Create axis
if len(x_axis) == len(y):
plt.plot(x_axis, y, label='Weight Scheme ' + str(number))
plt.plot(x_axis, y_index, label=market)
ax.xaxis.set_major_locator(
plt.MaxNLocator(5)) # Set Maximum number of x-axis values to show
fig.autofmt_xdate() # Rotate values to see more clearly
title = "Stock Selection: Growth of " + str(
initial_amount / 1000000) + " million"
plt.title(title)
plt.ylabel("Cumulative Return")
fig.savefig(directory + market + " Stock Selection Weight Scheme " +
str(number) + ".png")
plt.show()
# Write csv file of result matrix inside result folder
title = directory + 'weight scheme ' + str(number)
with open(title + ' result.csv', "w") as output:
writer = csv.writer(output, lineterminator='\n')
for val in resultMatrix:
writer.writerow(val)
df2 = pd.read_csv((directory + "weight scheme 2 result.csv"),
header=None,
low_memory=False)
result2 = np.array(df2[1:]) # Skip First Row
portfolio2 = result2[:, len(header)] # Last Column
portfolio2 = portfolio2[1:] # Delete 'Start From Below' part
# Calculate Quarterly Return (HK: semiannually)
quarter_matrix = [[0 for x in range(len(store_index))] for y in range(3)]
quarter_matrix[0][0] = "Semiannual Return"
quarter_matrix[1][0] = "Weight Scheme 1"
quarter_matrix[2][0] = "Weight Scheme 2"
for i in range(len(store_index) - 1):
quarter_matrix[0][i + 1] = store_index[i][1]
quarter_matrix[1][i + 1] = functions.percentageChange(
float(portfolio[store_index[i][0]]),
float(portfolio[store_index[i + 1][0]]))
quarter_matrix[2][i + 1] = functions.percentageChange(
float(portfolio2[store_index[i][0]]),
float(portfolio2[store_index[i + 1][0]]))
Quarter = quarter_matrix[0][1:]
Quarter_return1 = quarter_matrix[1][1:]
Quarter_return2 = quarter_matrix[2][1:]
# Draw quarterly return bar graphs
fig = plt.figure() # Create a figure
ax = plt.axes() # Create axis
plt.bar(Quarter, Quarter_return1, align='center', alpha=0.5) # Draw Bar graph
ax.xaxis.set_major_locator(
plt.MaxNLocator(8)) # Set Maximum number of x-axis values to show
def combined_signal(stock, weight_scheme, stock_index):
header = [
"Date", "Price", "Weight Signal", "Signal In", "Combined Signal",
"Signal Out", "Position", "Trading Cost", "Return(%)", "Cumulative(%)"
]
title = directory + functions.change_title(stock)
with open(title + ' technical indicators.csv') as f:
reader = csv.reader(f)
original = [rows for rows in reader]
original = np.array(original)
signal_matrix = [[0 for x in range(len(header))]
for y in range(len(original))]
signal_matrix[0] = header
signal_matrix = np.array(signal_matrix)
signal_matrix[:, 0][1:] = original[:, 0][1:] # Date
signal_matrix[:, 1][1:] = original[:, 1][1:] # Price
signal_matrix[:, 3][1:] = original[:, 8][1:] # signal in
signal_matrix[:, 5][1:] = original[:, 9][1:] # signal out
# Import Weight Scheme
with open('Stock Picking Result/' + weight_scheme + '.csv') as g:
reader = csv.reader(g)
weight = [rows for rows in reader]
weight = np.array(weight)
time_weight = weight[:, 0][1:]
timestamp = signal_matrix[:, 0][1:]
position = 0
portfolio = 1
for time in range(len(timestamp)):
answer = 0
month = int(timestamp[time].split("/")[1])
year = int(timestamp[time].split("/")[2])
# Q-2 quarter
if month < 4: # Q1
year -= 1
quarter = "Q2"
elif month < 10: # Q2 and Q3
year -= 1
quarter = "Q4"
else: # Q4
quarter = "Q2"
date = str(year) + quarter
# Search for index for weights
for index in range(len(time_weight)):
if date == time_weight[index]:
answer = index
if answer == 0:
stockweight = 0
else:
stockweight = float(weight[answer + 1][stock_index + 1]) / 100
# Fill in the weight signal in second column
if stockweight == 0:
signal_matrix[time + 1][2] = 0
weight_signal = 0
else:
signal_matrix[time + 1][2] = 1
weight_signal = 1
# Combined signal = 1 if both signal is 1
technical_signal = int(signal_matrix[time + 1][3])
if weight_signal == 1 and technical_signal == 1:
in_signal = 1
else:
in_signal = 0
signal_matrix[time + 1][4] = in_signal # Combined signal
# Position
out_signal = int(signal_matrix[time + 1][5])
if in_signal == 1 and out_signal == 1:
position = 0
elif in_signal == 1 and out_signal == 0:
position = 1
elif in_signal == 0 and out_signal == 1:
position = 0
if weight_signal == 0:
position = 0
signal_matrix[time + 1][6] = position # Position
# Trading Cost
if time != 0 and signal_matrix[time + 1][6] != signal_matrix[time][6]:
signal_matrix[time + 1][7] = trading_cost # Trading Cost
# Return
if time != 0:
gain = functions.percentageChange(
float(signal_matrix[time][1]),
float(signal_matrix[time + 1][1]))
gain *= int(signal_matrix[time][6]) # previous period's position
profit = gain - float(signal_matrix[time + 1][7]) # return - cost
signal_matrix[time + 1][8] = gain # return
# Cumulative Return
portfolio *= (1 + profit / 100)
cumulative = (portfolio - 1) * 100
signal_matrix[time + 1][9] = cumulative # cumulative return
# Write csv file of result matrix inside result folder
title = directory + functions.change_title(stock)
with open(title + ' combined signal ' + weight_scheme + '.csv',
"w") as output:
writer = csv.writer(output, lineterminator='\n')
for val in signal_matrix:
writer.writerow(val)
if sector[stock] == "Real Estate":
signal = int(ranking[answer][9])
signals.append(signal)
if sector[stock] == "Telecommunication Services":
signal = int(ranking[answer][10])
signals.append(signal)
if sector[stock] == "Utilities":
signal = int(ranking[answer][11])
signals.append(signal)
# Fill the result matrix with the return if signal = 1, otherwise zero
if index == 0: # Skip the first row
resultMatrix[index + 1][stock + 1] = 0
else:
gain = functions.percentageChange(
float(price[index - 1][stock + 1]),
float(price[index][stock + 1]))
resultMatrix[index + 1][stock + 1] = gain * signal
# Calculate number of stocks to be invested
if signal == 1 and float(price[index][stock + 1]) != 0:
stockcount += 1
market_cap_value = float(market_cap[answer_marketcap][stock + 1])
market_cap_list.append(market_cap_value)
market_cap_total += market_cap_value
else:
market_cap_list.append(0)
# Calculate Daily Return for each loop
row_to_calculate = resultMatrix[index + 1][1:len(ticker) +
1] # take the row
def market_timing(stock, pricelist, RSI_period, MACD_Fast, MACD_Slow, EMA_Slow,
RSI_Low, RSI_High, MACD_Sig, EMA_Fast):
header = [
"Date", "Price", "RSI", "MACD", "EMA", "RSI signal", "MACD Signal",
"EMA Signal", "Signal In", "Signal Out", "Position",
"Trading Cost (%)", "Return(%)", "Cumulative(%)"
]
price_matrix = [[0 for x in range(len(header))]
for y in range(len(timestamp) + 1)]
delete = functions.findzero(pricelist)
list_return = pricelist[:delete].tolist()
pricelist = pricelist[delete:]
RSI = rsi(pricelist, RSI_period)
MACD = macd(pricelist, MACD_Fast, MACD_Slow)
signal_line = ema(MACD, MACD_Sig)
EMA = ema(pricelist, EMA_Slow)
short_ema = ema(pricelist, EMA_Fast)
# Count index
index_macd = MACD_Slow - 1 + delete
index_signal_line = MACD_Slow - 1 + MACD_Sig - 1 + delete
index_ema = EMA_Slow - 1 + delete
index_short_ema = EMA_Fast - 1 + delete
index_rsi = RSI_period + delete
index_signal = max(index_rsi, index_signal_line, index_ema)
numberofday = len(price_matrix) - index_signal - 1
# Fill the headers
firstrow = price_matrix[0]
for integer in range(len(firstrow)):
firstrow[integer] = header[integer]
# Fill the rest of values
position = 0
cumulative = 0
portfolio = 1
for integer in range(len(price_matrix) - 1):
price_matrix[integer + 1][0] = timestamp[integer]
if integer >= delete:
signal_rsi = 0
signal_macd = 0
signal_ema = 0
price_matrix[integer + 1][1] = pricelist[integer - delete]
# RSI
if integer >= index_rsi:
value_rsi = RSI[integer - index_rsi]
price_matrix[integer + 1][2] = value_rsi
if RSI_Low < value_rsi < RSI_High:
signal_rsi = 1
else:
signal_rsi = 0
price_matrix[integer + 1][5] = signal_rsi
# MACD
if integer >= index_macd:
value_macd = MACD[integer - index_macd]
price_matrix[integer + 1][3] = value_macd
if integer >= index_signal_line:
value_signal_line = signal_line[integer -
index_signal_line]
if value_macd > value_signal_line:
signal_macd = 1
price_matrix[integer + 1][6] = signal_macd
# EMA
if integer >= index_short_ema:
value_short_ema = short_ema[integer - index_short_ema]
if integer >= index_ema:
value_ema = EMA[integer - index_ema]
price_matrix[integer + 1][4] = value_ema
if value_ema < value_short_ema:
signal_ema = 1
price_matrix[integer + 1][7] = signal_ema
# Signals and Positions
in_signal = 0
out_signal = 0
if integer >= index_signal:
# Signal In
if signal_rsi == 1 and signal_macd == 1 and signal_ema == 1:
in_signal = 1
price_matrix[integer + 1][8] = in_signal
# Signal Out
if signal_ema == 0:
out_signal = 1
price_matrix[integer + 1][9] = out_signal
# Position
if in_signal == 1 and out_signal == 1:
position = 0
elif in_signal == 1 and out_signal == 0:
position = 1
elif in_signal == 0 and out_signal == 1:
position = 0
price_matrix[integer + 1][10] = position
# Trading Cost
if price_matrix[integer + 1][10] != price_matrix[integer][10]:
price_matrix[integer + 1][11] = trading_cost
# Return
if integer > delete:
gain = functions.percentageChange(
pricelist[integer - delete - 1],
pricelist[integer - delete])
gain *= int(
price_matrix[integer][10]) # previous period's position
profit = gain - price_matrix[integer + 1][11] # return - cost
price_matrix[integer + 1][12] = gain
list_return.append(profit)
# Cumulative Return
portfolio *= (1 + profit / 100)
cumulative = (portfolio - 1) * 100
price_matrix[integer + 1][13] = cumulative
# Annualized Sharpe and Return
if numberofday != 0:
list_return = list_return[-numberofday:]
cumulative = cumulative * 252 / numberofday
sharpe = functions.Sharpe(list_return)
else:
cumulative = 0
sharpe = 0
# Write csv file of result matrix inside result folder
title = directory + functions.change_title(stock)
with open(title + ' technical indicators.csv', "w") as output:
writer = csv.writer(output, lineterminator='\n')
for val in price_matrix:
writer.writerow(val)
return [sharpe, cumulative]