def find_events(ls_symbols, d_data):
''' Finding the event dataframe '''
df_close = d_data['actual_close']
ts_market = df_close['SPY']
print "Finding Events"
# Creating an empty dataframe
df_events = copy.deepcopy(df_close)
df_events = df_events * np.NAN
# Time stamps for the event range
ldt_timestamps = df_close.index
print len(ldt_timestamps)
lookback = 20
bol_vals = bollinger.get_bollinger_values(ls_symbols, d_data, lookback)
spy_bol_val = bol_vals['SPY']
for s_sym in ls_symbols:
sym_bol_val = bol_vals[s_sym]
print len(sym_bol_val)
for i in range(0, len(ldt_timestamps) - 1):
# Get the bollinger values needed
sym_bol_today = sym_bol_val[i]
sym_bol_yest = sym_bol_val[i - 1]
spy_bol_today = spy_bol_val[i]
if sym_bol_today <= -2.0 and sym_bol_yest >= -2.0 and spy_bol_today >= 1.0:
df_events[s_sym].ix[ldt_timestamps[i]] = 1
return df_events
def find_events(ls_symbols, d_data):
''' Finding the event dataframe '''
df_close = d_data['actual_close']
ts_market = df_close['SPY']
print "Finding Events"
# Creating an empty dataframe
df_events = copy.deepcopy(df_close)
df_events = df_events * np.NAN
# Time stamps for the event range
ldt_timestamps = df_close.index
print len(ldt_timestamps)
lookback = 20
bol_vals = bollinger.get_bollinger_values(ls_symbols, d_data, lookback)
spy_bol_val = bol_vals['SPY']
for s_sym in ls_symbols:
sym_bol_val = bol_vals[s_sym]
print len(sym_bol_val)
for i in range(0, len(ldt_timestamps)-1):
# Get the bollinger values needed
sym_bol_today = sym_bol_val[i]
sym_bol_yest = sym_bol_val[i-1]
spy_bol_today = spy_bol_val[i]
if sym_bol_today <= -2.0 and sym_bol_yest >= -2.0 and spy_bol_today >= 1.0:
df_events[s_sym].ix[ldt_timestamps[i]] = 1
return df_events
def find_events(ls_symbols, d_data):
''' Finding the event dataframe '''
df_close = d_data['actual_close']
ts_market = df_close['SPY']
trades = []
# Creating an empty dataframe
df_events = copy.deepcopy(df_close)
df_events = df_events * np.NAN
# Time stamps for the event range
ldt_timestamps = df_close.index
lookback = 20
bol_vals = bollinger.get_bollinger_values(ls_symbols, d_data, lookback)
spy_bol_val = bol_vals['SPY']
for s_sym in ls_symbols:
sym_bol_val = bol_vals[s_sym]
for i in range(1, len(ldt_timestamps)):
# Get the bollinger values needed
sym_bol_today = sym_bol_val[i]
sym_bol_yest = sym_bol_val[i-1]
spy_bol_today = spy_bol_val[i]
if sym_bol_today <= -1.0 and sym_bol_yest >= -1.0 and spy_bol_today >= 1.0:
buy_date = return_dt(str(ldt_timestamps[i]))
if i+5 >= len(ldt_timestamps):
sell_date = return_dt(str(ldt_timestamps[-1]))
else:
sell_date = return_dt(str(ldt_timestamps[i+5]))
buy_trade = Trade(buy_date.group(1), buy_date.group(2), buy_date.group(3), s_sym, "Buy", 100)
sell_trade = Trade(sell_date.group(1), sell_date.group(2), sell_date.group(3), s_sym, "Sell", 100)
trades.append(buy_trade)
trades.append(sell_trade)
return trades
def find_events(ls_symbols, d_data):
""" Finding the event dataframe """
df_close = d_data["actual_close"]
ts_market = df_close["SPY"]
trades = []
# Creating an empty dataframe
df_events = copy.deepcopy(df_close)
df_events = df_events * np.NAN
# Time stamps for the event range
ldt_timestamps = df_close.index
lookback = 20
bol_vals = bollinger.get_bollinger_values(ls_symbols, d_data, lookback)
spy_bol_val = bol_vals["SPY"]
for s_sym in ls_symbols:
sym_bol_val = bol_vals[s_sym]
for i in range(1, len(ldt_timestamps)):
# Get the bollinger values needed
sym_bol_today = sym_bol_val[i]
sym_bol_yest = sym_bol_val[i - 1]
spy_bol_today = spy_bol_val[i]
if sym_bol_today <= -1.0 and sym_bol_yest >= -1.0 and spy_bol_today >= 1.0:
buy_date = return_dt(str(ldt_timestamps[i]))
if i + 5 >= len(ldt_timestamps):
sell_date = return_dt(str(ldt_timestamps[-1]))
else:
sell_date = return_dt(str(ldt_timestamps[i + 5]))
buy_trade = Trade(buy_date.group(1), buy_date.group(2), buy_date.group(3), s_sym, "Buy", 100)
sell_trade = Trade(sell_date.group(1), sell_date.group(2), sell_date.group(3), s_sym, "Sell", 100)
trades.append(buy_trade)
trades.append(sell_trade)
return trades
