def backTest(self):
''' Function that used to do back-testing based on the strategy you give
Params: None
Notes: this back-test function will move on minute bar and generate your
strategy detail dataframe by using the position vectors your strategy gives
each minute
'''
format1 = h5py.File(self.data_format1_path, mode='r')
format2 = h5py.File(self.data_format2_path, mode='r')
assets = list(format1.keys())
keys = list(format2.keys())
for i in range(len(keys)):
data_cur_min = format2[keys[i]][:]
# 1. initialization
if i == 0:
total_balance = self.init_cash
average_price_old = np.mean(data_cur_min[:, :4], axis=1)
position_old = np.repeat(0., 4)
position_new = np.repeat(0., 4)
details = list()
stop_signal = False
# 2. calculate position & cash/crypto/total balance & transaction cost etc.
position_change = position_new - position_old
mask = np.abs(position_change) > .25 * data_cur_min[:, 4]
position_change[mask] = (.25 * data_cur_min[:, 4] *
np.sign(position_change))[mask]
position_new = position_old + position_change
average_price = np.mean(data_cur_min[:, :4], axis=1)
transaction_cost = np.sum(
np.abs(position_change) * average_price * self.commissionRatio)
revenue = np.sum(
position_old *
(average_price - average_price_old)) - transaction_cost
crypto_balance = np.sum(np.abs(position_new * average_price))
total_balance = total_balance + revenue
cash_balance = total_balance - crypto_balance
detail = np.append(
position_new,
list(average_price) + [
cash_balance, crypto_balance, revenue, total_balance,
transaction_cost
])
details.append(copy.deepcopy(detail))
position_old = copy.deepcopy(position_new)
average_price_old = copy.deepcopy(average_price)
# 3. check special cases
# if cash balance is less than lower limit, the program will stop all trading actions in the future
if (cash_balance < self.cash_balance_lower_limit) and (stop_signal
== False):
stop_signal = True
print("Current cash balance is lower than",
self.cash_balance_lower_limit)
print("Your strategy is forced to stop")
print(
"System will soon close all your positions (long and short) on crypto currencies"
)
if stop_signal:
position_new = np.repeat(0., 4)
if '09:30:00' in keys[i]:
print(keys[i][:10])
continue
# Update position and memory
[position_new, self.memory
] = handle_bar(i, keys[i], data_cur_min, self.init_cash,
self.commissionRatio, cash_balance, crypto_balance,
total_balance, position_new, self.memory)
# Update position and timer
if '09:30:00' in keys[i]:
print(keys[i][:10])
detailCol = assets + [s + '_price' for s in assets] + [
"cash_balance", "crypto_balance", "revenue", "total_balance",
"transaction_cost"
]
detailsDF = pd.DataFrame(details,
index=pd.to_datetime(keys),
columns=detailCol)
format1.close()
format2.close()
return detailsDF
def backTest(self):
''' Function that used to do back-test based on the strategy you give
Params: None
Notes: this back-test function will move on minute bar and generate your
strategy detail dataframe by using the position matrix your strategy gives
each minute
Data:
1. Load futures information matrix and trading data during backtesting period
2. You can load different .h5 file by changing the path down below to paths like
self.train_data_path or self.test_data_path so that you can test and train your
own strategy on different periods
3. Try to make your strategy profitable and achieve stable return
'''
info = pd.read_csv(self.info_path, encoding='utf-8')
btData = h5py.File(self.data_format2_path, mode='r')
keys = list(btData.keys())
# PnL initialization
timer = 0
pnl = self.init_cash
details = list()
detail = [np.repeat(0.,13), self.init_cash, 0., 0., self.init_cash, 0.]
lastPosition = np.repeat(0, 13)
for i in range(len(keys)-1):
# Data of current minute and next minute
data_cur_min = btData[keys[i]][:]
exe_cur_min = np.mean(data_cur_min[:,:4], axis=1)
exe_next_min = np.mean(btData[keys[i+1]][:,:4], axis=1)
# Update position and parameter
[curPosition, self.memory] = handle_bar(timer, data_cur_min, info, self.init_cash, self.commissionRatio, detail, self.memory)
# Calculate margin required and commission charged
marginRequired = np.matmul(np.abs(curPosition), (exe_next_min * info.unit_per_lot * info.margin_rate))
transactionVolume = np.matmul(np.abs(curPosition-lastPosition), (exe_next_min * info.unit_per_lot))
commission = transactionVolume * self.commissionRatio
# Check and update pnl
dataChange = exe_next_min - exe_cur_min
revenue_min = np.matmul(lastPosition, dataChange * info.unit_per_lot)
pnl = pnl + revenue_min - commission
# Check if strategy losses too much
assert pnl > self.margin_threshold, "Too much loss, strategy failed"
# Check if margin requirement is satisfied
assert marginRequired <= pnl, "You don't have enough margin"
# Keep record
detail = [curPosition, pnl-marginRequired, marginRequired, revenue_min, pnl, commission]
details.append(copy.deepcopy(detail))
# Update position and timer
lastPosition = copy.deepcopy(curPosition)
timer+=1
if '09:30:00' in keys[i]:
print(keys[i][:10])
detailCol = ["postion","cash_balance", "margin_balance", "revenue", "total_balance", "transaction_cost"]
detailsDF = pd.DataFrame(details,index=pd.to_datetime(keys[:-1]),columns=detailCol)
btData.close()
return detailsDF
