def stats_perf(self, data):
# print date from and to
days_difference = data.tail(1)["close_time"].values[0] - data.head(
1)["open_time"].values[0]
days_difference = int(round(days_difference / np.timedelta64(1, 'D')))
time_from = data.head(1)["open_time"].astype(str).values[0]
time_to = data.tail(1)["close_time"].astype(str).values[0]
print("From {} to {} ({} days)\n".format(time_from, time_to,
days_difference))
# print total number of trades
# we can sum up every time a fee was applied to get total trades
total_trades = data.apply_fee.sum()
print("Total number of trades: {}".format(total_trades))
# print avg. trades per date
print("Avg. trades per day: {}".format(
round(total_trades / days_difference, 2)))
# print profit/loss (log returns)
cum_return = round(data["cum_pl"].iloc[-1] * 100, 2)
print("Profit/Loss [Log Return]: {0}%".format(cum_return))
# # print profit/loss (simple return)
# simple_return = (np.exp(data.iloc[-1].cum_pl) - 1) * 100
# print("Profit/Loss [Simple Return]: {0}%".format(round(simple_return, 2)))
# print maximum gains (log returns)
max_cum_pl = round(data["cum_pl"].max() * 100, 2)
print("Maximum Gain: {0}%".format(max_cum_pl))
#print maximum loss (log returns)
min_cum_pl = round(data["cum_pl"].min() * 100, 2)
print("Maximum Drawdown: {0}%".format(min_cum_pl))
# print sharpe ratio
# 96 (15 minutes in a day) and 365 days for the crypto market
# we compute the sharpe ratio based on profit and loss
sharpe_ratio = fs.sharpe_ratio(96 * 365, data.pl)
print("Annualised Sharpe Ratio: {0}".format(round(sharpe_ratio, 6)))
def stats_perf(self, data):
# print date from and to
time_from = data.head(1)["open_time"].astype(str).values[0]
time_to = data.tail(1)["close_time"].astype(str).values[0]
print("From {} to {}".format(time_from, time_to))
# print price bought at and current price
bought_price = data.iloc[0]["close"]
current_price = data.iloc[-1]["close"]
print("Bought at price {} and current price is {}\n".format(
round(bought_price, 5), round(current_price, 5)))
# print total number of trades, in this strategy always 1
print("Total number of trades: {}".format(1))
# print profit/loss (log returns)
cum_return = round(data["cum_pl"].iloc[-1] * 100, 2)
print("Profit/Loss [Log Return]: {0}%".format(cum_return))
# # print profit/loss (simple return)
# simple_return = (np.exp(data.iloc[-1].cum_pl) - 1) * 100
# print("Profit/Loss [Simple Return]: {0}%".format(round(simple_return, 2)))
# print maximum gains (log returns)
max_cum_pl = round(data["cum_pl"].max() * 100, 2)
print("Maximum Gain: {0}%".format(max_cum_pl))
#print maximum loss (log returns)
min_cum_pl = round(data["cum_pl"].min() * 100, 2)
print("Maximum Drawdown: {0}%".format(min_cum_pl))
# print sharpe ratio
# 96 (15 minutes in a day) and 365 days for the crypto market
# we compute the sharpe ratio based on profit and loss
sharpe_ratio = fs.sharpe_ratio(96 * 365, data.log_returns)
print("Annualised Sharpe Ratio: {0}".format(round(sharpe_ratio, 6)))