def orig_long_trend_bt(df,
b_df,
st_date,
ed_date,
sigs=(20, 50, 150),
transaction_cost=0,
plot=False,
index_name=None):
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 3.5), dpi=800)
ax0s = []
b_df = b_df[st_date:ed_date]
ret_b = b_df.pct_change().fillna(0)
port_ret = ret_b.rename('short').to_frame()
port_ret['long'] = bt_tools.trend_trading(
df, st_date, ed_date, sigs=sigs, transaction_cost=transaction_cost)
port_ret = port_ret.fillna(0)
ew_eq_curve = (port_ret + 1).cumprod()
# print(port_ret['long'])
## Long/ Short Side Plot
ax0s.append(ew_eq_curve['long'].plot(lw=0.75, ax=ax0, c='b'))
ax0s.append(ew_eq_curve['short'].plot(lw=0.75, ax=ax0, c='r'))
# plt.title(f"Long Short {index_name} Portfolio")
## combined PnL
combined_PnL = (port_ret['long'] - port_ret['short'] + 1).cumprod()
ax0s.append(combined_PnL.plot(lw=0.75, ax=ax1, c='g'))
if plot:
custom_lines = [
Line2D([0], [0], color='b', lw=0.75),
Line2D([0], [0], color='r', lw=0.75),
Line2D([0], [0], color='g', lw=0.75)
]
fig.legend(custom_lines, ['long', 'short', 'combined'],
ncol=len(custom_lines),
loc="upper center")
plt.setp(ax0s, ylabel='Cumulative Return')
plt.savefig(f"other_indices/{index_name}/pnls.png",
bbox_inches='tight') #
plt.close()
return ew_eq_curve['long'], ew_eq_curve['short']
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())
plt.close()
sigs=(20,50,150)
transaction_cost=0
# fig, (ax0,ax1) = plt.subplots(2, 1, figsize=(5, 3.5), dpi=800)
fig, ax1 = plt.subplots(1, 1, figsize=(5, 3.5), dpi=800)
b_df = b_df[st_date:ed_date]
ret_b = b_df.pct_change().fillna(0)
port_ret = ret_b.rename('sp500').to_frame()
port_ret['trend_cap_w'] = bt_tools.trend_trading(df,st_date, ed_date, sigs=sigs, transaction_cost=transaction_cost,w=w, signal_type='all')
port_ret['trend_ew'] = bt_tools.trend_trading(df,st_date, ed_date, sigs=sigs, transaction_cost=transaction_cost,w=None, signal_type='all')
# port_ret['mom'] = bt_tools.momentum_trading(df,st_date, ed_date)
port_ret = port_ret.fillna(0)
port_ret['sp500_cap_w'] = df.pct_change().mul(w).sum(axis=1)
port_ret['sp500_ew'] = df.pct_change().mean(axis=1)
ew_eq_curve = (port_ret + 1).cumprod()
import bt_tools
import pandas as pd
ed_date = '2020-09-13'
st_date = '2015-09-13'
start_bt_date_1yr_plus = '2012-09-13'
df = pd.DataFrame(
{
'A': [100, 101, 105, 106, 108],
'B': [101, 102, 106, 107, 105]
},
index=range(0, 5))
df.index.name = 'Date'
binary_signal = pd.DataFrame({
'A': [0, 1, 0, 1, 1],
'B': [0, 0, 0, 1, 1]
},
index=df.index)
ret = bt_tools.trend_trading(df, None, None, binary_signal=binary_signal)
print(ret)
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'])
})