def coherent_evolve(transaction_cost=0):
bt_st_date = "2020-09-13"
bt_date_range = [
"-".join([
str(pd.to_datetime(bt_st_date).year - i),
str(pd.to_datetime(bt_st_date).month),
str(pd.to_datetime(bt_st_date).day)
]) for i in range(0, 21, 5)
]
fig, axs = plt.subplots(4, 2, figsize=(10, 14), dpi=800)
cmaps = sns.diverging_palette(220, 20, center="dark", n=2)
ax0s = []
labels = []
perfs = pd.DataFrame(columns=pd.MultiIndex.from_product(
[['2020', '2015', '2010', '2005'], [(20, 50, 150), (30, 60, 160)]],
names=['end_year', 'trading_rules']))
for i, end_date in enumerate(bt_date_range[:-1]):
st_date = bt_date_range[i + 1]
start_bt_date_1yr_plus = "-".join([
str(pd.to_datetime(st_date).year - 1),
str(pd.to_datetime(st_date).month),
str(pd.to_datetime(st_date).day)
])
df, b_df = get_df_stoxx600(start_bt_date_1yr_plus, end_date)
b_df = b_df[st_date:end_date]
ret_b = b_df.pct_change().fillna(0)
port_ret = ret_b.rename('short').to_frame()
## index
ew_eq_curve = (1 + ret_b).cumprod().rename('short').to_frame()
ax0s.append(ew_eq_curve['short'].plot(ax=axs[i, 0],
lw=0.75,
c='g',
label='index'))
for s, sigs in enumerate([(20, 50, 150), (30, 60, 160)]):
port_ret['long'] = trend_trading(df,
st_date,
end_date,
sigs=sigs,
transaction_cost=0)
port_ret = port_ret.fillna(0)
ew_eq_curve = (port_ret + 1).cumprod()
ax0s.append(ew_eq_curve['long'].plot(ax=axs[i, 0],
lw=0.75,
c=cmaps[s]))
## combined PnL
combined_PnL = (port_ret['long'] - port_ret['short'] + 1).cumprod()
combined_PnL.plot(ax=axs[i, 1], c=cmaps[s])
perfs[(end_date[:4], sigs)] = port_stats(ew_eq_curve['long'],
ew_eq_curve['short'])
# print(port_ret['long'])
# print(port_stats(ew_eq_curve['long'], ew_eq_curve['short']))
custom_lines = [
Line2D([0], [0], color=cmaps[0], lw=0.75),
Line2D([0], [0], color=cmaps[1], lw=0.75),
Line2D([0], [0], color='g', lw=0.75)
]
fig.legend(custom_lines,
['long (20,50,150)', 'long (30,60,160)', 'short index'],
ncol=len(custom_lines),
loc="upper center")
plt.setp(ax0s, ylabel='Cumulative Return')
# plt.show()
plt.savefig(f"stress_trend_follow/coherent_evolve.png",
bbox_inches='tight') #
plt.close()
# perfs = perfs.T
with pd.option_context('display.max_rows', None, 'display.max_columns',
None):
print(perfs.T.to_latex())
r1 = perfs.iloc[:,
perfs.columns.get_level_values(1) == (
30, 60, 160)].droplevel('trading_rules', axis=1)
r2 = perfs.iloc[:,
perfs.columns.get_level_values(1) == (
20, 50, 150)].droplevel('trading_rules', axis=1)
print((r1 - r2).sort_index(axis=1).to_latex())
def structural_pred(start_bt_date='2007-01-01', end_bt_date='2010-01-01'):
'''
deviate from the optimal trading rule by using
signal_{x} > signal_{x+1} > signal_{x+2}.
:param sigs:
:param start_bt_date:
:param end_bt_date:
:return:
'''
start_bt_date_1yr_plus = "-".join(
[str(int(start_bt_date.split('-')[0]) - 1)] +
start_bt_date.split('-')[1:])
df, stoxx600 = get_df_stoxx600(
start_bt_date_1yr_plus=start_bt_date_1yr_plus, end_bt_date=end_bt_date)
transaction_cost = 0.0000
ret_stoxx600 = stoxx600[start_bt_date:].pct_change().fillna(0)
eq_curve_stoxx600 = (ret_stoxx600 + 1).cumprod()
sigs = (30, 60, 160)
closing_period = 100
# deviations = list(zip(list(range(sigs[0]+2, closing_period+2)), list(range(sigs[1]+1, closing_period+1)), [sigs[2]] * closing_period))
# deviations.insert(0,sigs)
deviations = [(20, 50, 150), (30, 59, 158), (30, 60, 160),
(32, 61, 160)] #, (30,58,157) ,(40,65,160)
cmaps = sns.diverging_palette(220, 20, center="dark", n=len(deviations))
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 3.5), dpi=800)
# fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(100, 50))
ax0s = []
labels = []
long_end_PnLs = []
plt_freq = 1
for i, new_sigs in enumerate(deviations):
temp_fast, temp_mid, temp_slow = trend_follow_sigs(df, new_sigs)
long_signal = ((temp_fast > temp_mid) &
(temp_mid > temp_slow)).astype(int)
long_holdings = long_signal.shift(1)[start_bt_date:]
ret_df = df[start_bt_date:].pct_change(1).fillna(0)
arr_transaction_cost = [0, 0, 0, 0, transaction_cost] * (
len(ret_df.index) // 5) + [0] * (len(ret_df.index) % 5)
## equal weighting
long_w = (1 / long_holdings.sum(axis=1)).replace([np.inf, -np.inf], 0)
port_ret = (long_holdings.mul(long_w, axis='index') *
ret_df).sum(axis=1).rename('long').to_frame()
port_ret['benchmark'] = ret_stoxx600
port_ret = port_ret.fillna(0)
## Long/ Short Side Plot
ew_eq_curve = (port_ret + 1).cumprod()
# plt_freq = 10 if direction == 'Neg Deviations' else 30
# print(f"new sigs {new_sigs}, {type(new_sigs[0])}")
if i % plt_freq == 0 or new_sigs == [30, 60, 160]:
ax0s.append(ew_eq_curve['long'].plot(ax=ax0, lw=0.75, c=cmaps[i]))
if new_sigs == (20, 50, 150):
labels.append(f"{','.join(map(str, new_sigs))} (benchmark)")
else:
labels.append(",".join(map(str, new_sigs)))
## combined PnL
combined_PnL = (port_ret['long'] - port_ret['benchmark'] -
arr_transaction_cost + 1).cumprod()
combined_PnL.plot(ax=ax1, lw=0.70, c=cmaps[i])
long_end_PnLs.append(
(",".join(map(str,
new_sigs)), ew_eq_curve['long'].tail(1).values[0]))
# ax0.legend(loc='upper center', fontsize='small', ncol=len(deviations)//5)
# ax1.legend(loc='upper center', fontsize='small', ncol=len(deviations)//5)
b = eq_curve_stoxx600.plot(ax=ax0, lw=0.70, c='g', label='index')
ax0s.append(b)
labels.append("index")
fig.legend(
ax0s, # The line objects
labels=labels, # The labels for each line
loc="upper center", # Position of legend
borderaxespad=0.1, # Small spacing around legend box
# fontsize='small',
ncol=len(labels))
plt.setp(ax0s, ylabel='Cumulative Return')
# plt.show()
plt.savefig(f"stress_trend_follow/pnl_struct_pred.png",
bbox_inches='tight')
plt.close()
import bt_tools
import pandas as pd
import matplotlib.pyplot as plt
from risk_parity import risk_parity_weighting
ed_date = '2020-09-13'
st_date = '2015-09-13'
st_two_date = '2014-09-13'
start_bt_date_1yr_plus = '2012-09-13'
# df, b_df = bt_tools.get_df_sp500(start_bt_date_1yr_plus, ed_date)
df, b_df = bt_tools.get_df_stoxx600(start_bt_date_1yr_plus, ed_date)
# df, b_df = bt_tools.get_df_ftse250(start_bt_date_1yr_plus, ed_date)
## index returns
port_df = b_df.loc[st_date:ed_date].pct_change().fillna(0).rename(
'short').to_frame()
sigs = (30, 60, 160)
fast_MA, mid_MA, slow_MA = bt_tools.trend_follow_sigs(df, sigs)
entry_long = ((fast_MA > mid_MA) & (mid_MA > slow_MA)).astype(int)
exit_long = ((fast_MA < mid_MA) & (mid_MA > slow_MA)).astype(int)
pos_df_long = bt_tools.get_position_df(df, entry_long, exit_long)
# eq_risk_w_long = bt_tools.equal_risk_weighting(df, pos_df_long, st_date, ed_date)
# eq_risk_w_long.to_pickle('eq_risk_w_long.pkl')
eq_risk_w_long = pd.read_pickle("eq_risk_w_long.pkl")
def stress_trend_follow(direction='Pos Deviations',
start_bt_date='2007-01-01',
end_bt_date='2010-01-01'):
dev_name = direction ##
dd_devs = {
'Pos Deviations': range(0, 51, 1),
'Neg Deviations': range(0, -21, -1)
}
start_bt_date_1yr_plus = "-".join(
[str(int(start_bt_date.split('-')[0]) - 1)] +
start_bt_date.split('-')[1:])
df, stoxx600 = get_df_stoxx600(
start_bt_date_1yr_plus=start_bt_date_1yr_plus, end_bt_date=end_bt_date)
transaction_cost = 0.0010
ret_stoxx600 = stoxx600[start_bt_date:].pct_change().fillna(0)
eq_curve_stoxx600 = (ret_stoxx600 + 1).cumprod()
sigs = [20, 50, 150]
deviations = dd_devs[dev_name]
cmaps = sns.diverging_palette(250,
15,
s=75,
l=40,
center="dark",
n=len(deviations))
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 3.5), dpi=800)
ax0s = []
labels = []
long_end_PnLs = []
long_mean_Sharpe = []
for i, plus_t in enumerate(deviations):
new_sigs = list(map(lambda x: x + plus_t, sigs))
temp_fast, temp_mid, temp_slow = trend_follow_sigs(df, new_sigs)
long_signal = ((temp_fast > temp_mid) &
(temp_mid > temp_slow)).astype(int)
long_holdings = long_signal.shift(1)[start_bt_date:]
ret_df = df[start_bt_date:].pct_change(1).fillna(0)
arr_transaction_cost = [0, 0, 0, 0, transaction_cost] * (
len(ret_df.index) // 5) + [0] * (len(ret_df.index) % 5)
## equal weighting
long_w = (1 / long_holdings.sum(axis=1)).replace([np.inf, -np.inf], 0)
port_ret = (long_holdings.mul(long_w, axis='index') *
ret_df).sum(axis=1).rename('long').to_frame()
port_ret['index'] = ret_stoxx600
port_ret = port_ret.fillna(0)
## Long/ Short Side Plot
ew_eq_curve = (port_ret + 1).cumprod()
plt_freq = 10 if direction == 'Neg Deviations' else 30
# print(f"new sigs {new_sigs}, {type(new_sigs[0])}")
if i % plt_freq == 0 or new_sigs == [30, 60, 160]:
ax0s.append(ew_eq_curve['long'].plot(ax=ax0, lw=0.75, c=cmaps[i]))
if new_sigs == [20, 50, 150]:
labels.append(f"{','.join(map(str,new_sigs))} (benchmark)")
else:
labels.append(",".join(map(str, new_sigs)))
## combined PnL
combined_PnL = (port_ret['long'] - port_ret['index'] -
arr_transaction_cost + 1).cumprod()
combined_PnL.plot(ax=ax1, lw=0.70, c=cmaps[i])
## bar EndPnL
long_end_PnLs.append(
(",".join(map(str,
new_sigs)), ew_eq_curve['long'].tail(1).values[0]))
long_mean_Sharpe.append(
(",".join(map(str, new_sigs)), sharpe(ew_eq_curve['long'])))
# ax0.legend(loc='upper center', fontsize='small', ncol=len(deviations)//5)
# ax1.legend(loc='upper center', fontsize='small', ncol=len(deviations)//5)
b = eq_curve_stoxx600.plot(ax=ax0, lw=0.70, c='g', label='index')
ax0s.append(b)
labels.append("index")
fig.legend(
ax0s, # The line objects
labels=labels, # The labels for each line
loc="upper center", # Position of legend
borderaxespad=0.1, # Small spacing around legend box
# fontsize='small',
ncol=len(labels))
plt.setp(ax0s, ylabel='Cumulative Return')
# plt.show()
plt.savefig(
f"stress_trend_follow/pnl_{direction.lower().replace(' ','_')}.png",
bbox_inches='tight')
plt.close()
## Bar mean Sharpe ratio
long_mean_Sharpe = pd.DataFrame(long_mean_Sharpe,
columns=['sig', 'sharpe'])
fig, ax = plt.subplots(1, 1, figsize=(5, 3.5), dpi=800)
ax.bar(long_mean_Sharpe['sig'], long_mean_Sharpe['sharpe'])
plt.xticks(rotation=90, fontsize=6)
plt.ylabel('avg. annual Sharpe')
# plt.show()
plt.savefig(
f"stress_trend_follow/bar_mean_sharpe_{direction.lower().replace(' ', '_')}.png",
bbox_inches='tight')
plt.close()
## Bar End PnL
long_end_PnLs = pd.DataFrame(long_end_PnLs, columns=['sig', 'PnL'])
fig, ax = plt.subplots(1, 1, figsize=(5, 3.5), dpi=800)
ax.bar(long_end_PnLs['sig'], long_end_PnLs['PnL'])
plt.xticks(rotation=90, fontsize=6)
plt.ylabel('End Cumulative Return')
# plt.show()
plt.savefig(
f"stress_trend_follow/bar_end_{direction.lower().replace(' ', '_')}.png",
bbox_inches='tight')
plt.close()
def long_short_trend(transaction_cost=0.0000, plot=False):
start_bt_date_1yr_plus = '2014-09-13'
start_bt_date = '2015-09-13'
end_bt_date = '2020-09-13'
df, stoxx600 = get_df_stoxx600(
start_bt_date_1yr_plus=start_bt_date_1yr_plus, end_bt_date=end_bt_date)
# df, stoxx600 = get_df_sp500(start_bt_date_1yr_plus=start_bt_date_1yr_plus, end_bt_date=end_bt_date)
## short index
port_ret = stoxx600.loc[start_bt_date:end_bt_date].pct_change().rename(
'short_index').to_frame()
## sides return
port_ret['long'] = trend_trading(df,
start_bt_date,
end_bt_date,
sigs=(20, 50, 150),
transaction_cost=0)
port_ret['short_trend'] = trend_trading(df,
start_bt_date,
end_bt_date,
sigs=(20, 50, 150),
transaction_cost=0,
direction='short')
port_ret = port_ret.fillna(0)
## short trend side
plt.rcParams["figure.dpi"] = 800
with pd.option_context('display.max_rows', None, 'display.max_columns',
None):
print(port_ret['short_trend'].sort_values(ascending=False))
(-port_ret['short_trend'].fillna(0) + 1).cumprod().plot(
figsize=(5, 3.5), legend=True, lw=0.75, fontsize=10).legend(loc=2)
plt.ylabel('Cumulative Return')
plt.savefig("short_trend_follow/short_side_pnl.png")
plt.close()
## ew_eq_curves
eq_eq_curves = (port_ret + 1).cumprod()
plt.rcParams["figure.dpi"] = 800
eq_eq_curves.plot(figsize=(5, 3.5), legend=True, lw=0.75,
fontsize=10).legend(loc=2)
plt.ylabel('Cumulative Return')
plt.savefig("short_trend_follow/pnls")
plt.close()
## combined Pnl
plt.rcParams["figure.dpi"] = 800
combined_PnL = (
port_ret['long'] -
port_ret['short_trend']).rename('long_short_trend').to_frame()
combined_PnL['benchmark'] = (port_ret['long'] - port_ret['short_index'])
combined_PnL = (combined_PnL.fillna(0) + 1).cumprod()
combined_PnL.plot(figsize=(5, 3.5), legend=True, lw=0.75,
fontsize=10).legend(loc=2)
plt.ylabel('Cumulative Return')
plt.savefig("short_trend_follow/combined_pnl")
plt.close()
# eq_eq_curves['long'] = (port_ret['long'] +1).cumprod()
# eq_eq_curves['short'] = (port_ret['short'] +1).cumprod()
## performance_analysis
performance_analysis(eq_eq_curves['long'], eq_eq_curves['short_trend'])
## portfolio analysis
return pd.DataFrame({
'benchmark':
port_stats(eq_eq_curves['long'], eq_eq_curves['short_index']),
'long_short_trend':
port_stats(eq_eq_curves['long'], eq_eq_curves['short_trend'])
})