def trades(trades_list: list, daily_balance: list) -> dict:
starting_balance = 0
current_balance = 0
for e in store.exchanges.storage:
starting_balance += store.exchanges.storage[e].starting_assets[
jh.app_currency()]
current_balance += store.exchanges.storage[e].assets[jh.app_currency()]
if len(trades_list) == 0:
return None
df = pd.DataFrame.from_records([t.to_dict() for t in trades_list])
total_completed = len(df)
winning_trades = df.loc[df['PNL'] > 0]
total_winning_trades = len(winning_trades)
losing_trades = df.loc[df['PNL'] < 0]
total_losing_trades = len(losing_trades)
arr = df['PNL'].to_numpy()
pos = np.clip(arr, 0, 1).astype(bool).cumsum()
neg = np.clip(arr, -1, 0).astype(bool).cumsum()
current_streak = np.where(
arr >= 0, pos - np.maximum.accumulate(np.where(arr <= 0, pos, 0)),
-neg + np.maximum.accumulate(np.where(arr >= 0, neg, 0)))
s_min = current_streak.min()
losing_streak = 0 if s_min > 0 else abs(s_min)
s_max = current_streak.max()
winning_streak = 0 if s_max < 0 else s_max
largest_losing_trade = df['PNL'].min()
largest_winning_trade = df['PNL'].max()
win_rate = len(winning_trades) / (len(losing_trades) + len(winning_trades))
max_R = df['R'].max()
min_R = df['R'].min()
mean_R = df['R'].mean()
longs_count = len(df.loc[df['type'] == 'long'])
shorts_count = len(df.loc[df['type'] == 'short'])
longs_percentage = longs_count / (longs_count + shorts_count) * 100
short_percentage = 100 - longs_percentage
fee = df['fee'].sum()
net_profit = df['PNL'].sum()
net_profit_percentage = (net_profit / starting_balance) * 100
average_win = winning_trades['PNL'].mean()
average_loss = abs(losing_trades['PNL'].mean())
ratio_avg_win_loss = average_win / average_loss
expectancy = (0 if np.isnan(average_win) else average_win) * win_rate - (
0 if np.isnan(average_loss) else average_loss) * (1 - win_rate)
expectancy = expectancy
expectancy_percentage = (expectancy / starting_balance) * 100
expected_net_profit_every_100_trades = expectancy_percentage * 100
average_holding_period = df['holding_period'].mean()
average_winning_holding_period = winning_trades['holding_period'].mean()
average_losing_holding_period = losing_trades['holding_period'].mean()
gross_profit = df.loc[df['PNL'] > 0]['PNL'].sum()
gross_loss = df.loc[df['PNL'] < 0]['PNL'].sum()
daily_returns = pd.Series(daily_balance).pct_change(1).values
max_drawdown = crypto_empyrical.max_drawdown(daily_returns) * 100
annual_return = crypto_empyrical.annual_return(daily_returns) * 100
sharpe_ratio = crypto_empyrical.sharpe_ratio(daily_returns)
calmar_ratio = crypto_empyrical.calmar_ratio(daily_returns)
sortino_ratio = crypto_empyrical.sortino_ratio(daily_returns)
omega_ratio = crypto_empyrical.omega_ratio(daily_returns)
total_open_trades = store.app.total_open_trades
open_pl = store.app.total_open_pl
return {
'total':
np.nan if np.isnan(total_completed) else total_completed,
'total_winning_trades':
np.nan if np.isnan(total_winning_trades) else total_winning_trades,
'total_losing_trades':
np.nan if np.isnan(total_losing_trades) else total_losing_trades,
'starting_balance':
np.nan if np.isnan(starting_balance) else starting_balance,
'finishing_balance':
np.nan if np.isnan(current_balance) else current_balance,
'win_rate':
np.nan if np.isnan(win_rate) else win_rate,
'max_R':
np.nan if np.isnan(max_R) else max_R,
'min_R':
np.nan if np.isnan(min_R) else min_R,
'mean_R':
np.nan if np.isnan(mean_R) else mean_R,
'ratio_avg_win_loss':
np.nan if np.isnan(ratio_avg_win_loss) else ratio_avg_win_loss,
'longs_count':
np.nan if np.isnan(longs_count) else longs_count,
'longs_percentage':
np.nan if np.isnan(longs_percentage) else longs_percentage,
'short_percentage':
np.nan if np.isnan(short_percentage) else short_percentage,
'shorts_count':
np.nan if np.isnan(shorts_count) else shorts_count,
'fee':
np.nan if np.isnan(fee) else fee,
'net_profit':
np.nan if np.isnan(net_profit) else net_profit,
'net_profit_percentage':
np.nan if np.isnan(net_profit_percentage) else net_profit_percentage,
'average_win':
np.nan if np.isnan(average_win) else average_win,
'average_loss':
np.nan if np.isnan(average_loss) else average_loss,
'expectancy':
np.nan if np.isnan(expectancy) else expectancy,
'expectancy_percentage':
np.nan if np.isnan(expectancy_percentage) else expectancy_percentage,
'expected_net_profit_every_100_trades':
np.nan if np.isnan(expected_net_profit_every_100_trades) else
expected_net_profit_every_100_trades,
'average_holding_period':
average_holding_period,
'average_winning_holding_period':
average_winning_holding_period,
'average_losing_holding_period':
average_losing_holding_period,
'gross_profit':
gross_profit,
'gross_loss':
gross_loss,
'max_drawdown':
max_drawdown,
'annual_return':
annual_return,
'sharpe_ratio':
sharpe_ratio,
'calmar_ratio':
calmar_ratio,
'sortino_ratio':
sortino_ratio,
'omega_ratio':
omega_ratio,
'total_open_trades':
total_open_trades,
'open_pl':
open_pl,
'winning_streak':
winning_streak,
'losing_streak':
losing_streak,
'largest_losing_trade':
largest_losing_trade,
'largest_winning_trade':
largest_winning_trade,
'current_streak':
current_streak[-1],
}
def trades(trades_list: list, daily_balance: list):
starting_balance = 0
current_balance = 0
for e in store.exchanges.storage:
starting_balance += store.exchanges.storage[e].starting_assets[
jh.app_currency()]
current_balance += store.exchanges.storage[e].assets[jh.app_currency()]
starting_balance = round(starting_balance, 2)
current_balance = round(current_balance, 2)
if len(trades_list) == 0:
return None
df = pd.DataFrame.from_records([t.to_dict() for t in trades_list])
total_completed = len(df)
winning_trades = df.loc[df['PNL'] > 0]
total_winning_trades = len(winning_trades)
losing_trades = df.loc[df['PNL'] < 0]
total_losing_trades = len(losing_trades)
losing_i = df['PNL'] < 0
losing_streaks = losing_i.ne(losing_i.shift()).cumsum()
losing_streak = losing_streaks[losing_i].value_counts().max()
winning_i = df['PNL'] > 0
winning_streaks = winning_i.ne(winning_i.shift()).cumsum()
winning_streak = winning_streaks[winning_i].value_counts().max()
largest_losing_trade = round(df['PNL'].min(), 2)
largest_winning_trade = round(df['PNL'].max(), 2)
win_rate = len(winning_trades) / (len(losing_trades) + len(winning_trades))
max_R = round(df['R'].max(), 2)
min_R = round(df['R'].min(), 2)
mean_R = round(df['R'].mean(), 2)
longs_count = len(df.loc[df['type'] == 'long'])
shorts_count = len(df.loc[df['type'] == 'short'])
longs_percentage = longs_count / (longs_count + shorts_count) * 100
short_percentage = 100 - longs_percentage
fee = df['fee'].sum()
net_profit = round(df['PNL'].sum(), 2)
net_profit_percentage = round((net_profit / starting_balance) * 100, 2)
average_win = round(winning_trades['PNL'].mean(), 2)
average_loss = round(abs(losing_trades['PNL'].mean()), 2)
ratio_avg_win_loss = average_win / average_loss
expectancy = (0 if np.isnan(average_win) else average_win) * win_rate - (
0 if np.isnan(average_loss) else average_loss) * (1 - win_rate)
expectancy = round(expectancy, 2)
expectancy_percentage = round((expectancy / starting_balance) * 100, 2)
expected_net_profit_every_100_trades = round(expectancy_percentage * 100,
2)
average_holding_period = df['holding_period'].mean()
average_winning_holding_period = winning_trades['holding_period'].mean()
average_losing_holding_period = losing_trades['holding_period'].mean()
gross_profit = round(df.loc[df['PNL'] > 0]['PNL'].sum(), 2)
gross_loss = round(df.loc[df['PNL'] < 0]['PNL'].sum(), 2)
daily_returns = pd.Series(daily_balance).pct_change(1).values
max_drawdown = round(crypto_empyrical.max_drawdown(daily_returns) * 100, 2)
annual_return = round(
crypto_empyrical.annual_return(daily_returns) * 100, 2)
sharpe_ratio = round(crypto_empyrical.sharpe_ratio(daily_returns), 2)
calmar_ratio = round(crypto_empyrical.calmar_ratio(daily_returns), 2)
sortino_ratio = round(crypto_empyrical.sortino_ratio(daily_returns), 2)
omega_ratio = round(crypto_empyrical.omega_ratio(daily_returns), 2)
total_open_trades = store.app.total_open_trades
open_pl = store.app.total_open_pl
return {
'total':
np.nan if np.isnan(total_completed) else total_completed,
'total_winning_trades':
np.nan if np.isnan(total_winning_trades) else total_winning_trades,
'total_losing_trades':
np.nan if np.isnan(total_losing_trades) else total_losing_trades,
'starting_balance':
np.nan if np.isnan(starting_balance) else starting_balance,
'finishing_balance':
np.nan if np.isnan(current_balance) else current_balance,
'win_rate':
np.nan if np.isnan(win_rate) else win_rate,
'max_R':
np.nan if np.isnan(max_R) else max_R,
'min_R':
np.nan if np.isnan(min_R) else min_R,
'mean_R':
np.nan if np.isnan(mean_R) else mean_R,
'ratio_avg_win_loss':
np.nan if np.isnan(ratio_avg_win_loss) else ratio_avg_win_loss,
'longs_count':
np.nan if np.isnan(longs_count) else longs_count,
'longs_percentage':
np.nan if np.isnan(longs_percentage) else longs_percentage,
'short_percentage':
np.nan if np.isnan(short_percentage) else short_percentage,
'shorts_count':
np.nan if np.isnan(shorts_count) else shorts_count,
'fee':
np.nan if np.isnan(fee) else fee,
'net_profit':
np.nan if np.isnan(net_profit) else net_profit,
'net_profit_percentage':
np.nan if np.isnan(net_profit_percentage) else net_profit_percentage,
'average_win':
np.nan if np.isnan(average_win) else average_win,
'average_loss':
np.nan if np.isnan(average_loss) else average_loss,
'expectancy':
np.nan if np.isnan(expectancy) else expectancy,
'expectancy_percentage':
np.nan if np.isnan(expectancy_percentage) else expectancy_percentage,
'expected_net_profit_every_100_trades':
np.nan if np.isnan(expected_net_profit_every_100_trades) else
expected_net_profit_every_100_trades,
'average_holding_period':
average_holding_period,
'average_winning_holding_period':
average_winning_holding_period,
'average_losing_holding_period':
average_losing_holding_period,
'gross_profit':
gross_profit,
'gross_loss':
gross_loss,
'max_drawdown':
max_drawdown,
'annual_return':
annual_return,
'sharpe_ratio':
sharpe_ratio,
'calmar_ratio':
calmar_ratio,
'sortino_ratio':
sortino_ratio,
'omega_ratio':
omega_ratio,
'total_open_trades':
total_open_trades,
'open_pl':
open_pl,
'winning_streak':
winning_streak,
'losing_streak':
losing_streak,
'largest_losing_trade':
largest_losing_trade,
'largest_winning_trade':
largest_winning_trade,
}