def part_test_fun(instrument):
adj_close_df = FutData().load_act_adj_fut_data(instrument, 'Close', active_df, aadj_factor_df)
adj_high_df = FutData().load_act_adj_fut_data(instrument, 'High', active_df, aadj_factor_df)
adj_low_df = FutData().load_act_adj_fut_data(instrument, 'Low', active_df, aadj_factor_df)
adj_return_df = adj_close_df/adj_close_df.shift(1) - 1
for n in window_list:
raw_factor = FutIndex.boll_fun(adj_close_df, n)
for limit in limit_list:
signal = Signal.fun_1(raw_factor, limit)
pos = Position.fun_1(signal)
pnl_df = (pos.shift(2) * adj_return_df).fillna(0)
sp = bt.AZ_Sharpe_y(pnl_df)
pot = bt.AZ_Pot(pos, pnl_df.sum())
print(instrument, sp, pot, n, limit)
if abs(sp) > 1.4:
print('!!!!!!!!!!!!!!!!')
def part_test(
con_id,
begin_time,
end_time,
# p_window, p_limit,
# v_window, v_limit,
# voli_window, voli_limit,
CCI_window,
CCI_limit,
# hold_time
):
try:
# print(con_id, begin_time, end_time)
data_df = fut_data.load_intra_data(con_id,
['High', 'Low', 'Close', 'Volume'])
# begin_time = pd.to_datetime('20190101')
# # end_time = begin_time + timedelta(1)
# end_time = pd.to_datetime('20190401')
# p_window = 1
# p_limit = 1
# v_window = 20
# v_limit = 2
part_data_df = data_df.truncate(before=begin_time, after=end_time)
part_data_df['weekday'] = pd.to_datetime(
part_data_df['Date']).dt.weekday
part_data_df['weekday_pos'] = (part_data_df['weekday'] !=
2).astype(int)
part_data_df['month'] = part_data_df['Date'].str.slice(5, 7)
# part_data_df['month_pos'] = ((part_data_df['month'] != '2') &
# (part_data_df['month'] != '5')).astype(int)
v_window = 60
part_data_df['Volume_zscore'] = bt.AZ_Col_zscore(
part_data_df[['Volume']], v_window)
part_data_df['Volume_signal'] = (part_data_df['Volume_zscore'] <
2).astype(int).replace(0, -1)
p_window = 3
part_data_df['past_min_pct_change'] = (part_data_df['Close'] - part_data_df['Close'].shift(p_window)) \
/ part_data_df['Close'].shift(p_window)
# part_data_df['past_min_pct_change'] = (part_data_df['Close'] - part_data_df['Close'].shift(p_window))
part_data_df['past_min_pct_signal'] = part_data_df['past_min_pct_change'] \
.apply(lambda x: 0 if abs(x) < 0.005 else (1 if x > 0.005 else -1))
part_data_df['CCI_score'] = FutIndex.CCI_fun(part_data_df['High'],
part_data_df['Low'],
part_data_df['Close'],
CCI_window)
part_data_df['CCI_signal'] = Signal.fun_1(part_data_df['CCI_score'],
CCI_limit)
part_data_df['CCI_pos'] = Position.fun_1(part_data_df['CCI_signal'])
part_data_df['boll_score'] = FutIndex.boll_fun(part_data_df['Close'],
CCI_window)
part_data_df['boll_signal'] = Signal.fun_1(part_data_df['boll_score'],
CCI_limit)
part_data_df['boll_pos'] = Position.fun_1(part_data_df['boll_signal'])
part_data_df['trend_signal'] = bt.AZ_Rolling(part_data_df['Close'],
500).std()
part_data_df['trend_pos'] = (part_data_df['trend_signal'] <
45).astype(int).replace(0, -1)
part_data_df['position'] = part_data_df['boll_pos']
part_data_df['position_sft'] = part_data_df['position'].shift(
2) * part_data_df['weekday_pos']
part_data_df['price_return'] = part_data_df['Close'] - part_data_df[
'Close'].shift(1)
# part_data_df['price_return_sum'] = bt.AZ_Rolling(part_data_df['price_return'], hold_time).sum() \
# .shift(-hold_time + 1)
part_data_df['pnl'] = part_data_df['position_sft'] * part_data_df[
'price_return']
part_data_df['asset'] = part_data_df['pnl'].cumsum()
# part_data_df['pnl_test'] = part_data_df['signal'] * part_data_df['price_return_sum'].shift(-2)
part_data_df['turnover'] = (part_data_df['position_sft'] - part_data_df['position_sft'].shift(1)) \
* part_data_df['Close']
# 剔除开盘收盘5min的signal
part_pnl_df = part_data_df.groupby('Date')['pnl'].sum()
part_turnover = part_data_df.groupby('Date')['turnover'].apply(
lambda x: sum(abs(x)))
return part_pnl_df, part_turnover, part_data_df
except Exception as error:
print(error)
return None
print('!!!!!!!!!!!!!!!!')
window_list = [10, 20, 40, 100]
limit_list = [1, 1.5, 2]
for instrument in instrument_list:
# part_test_fun(instrument)
adj_close_df = FutData().load_act_adj_fut_data(instrument, 'Close', active_df, aadj_factor_df)
adj_high_df = FutData().load_act_adj_fut_data(instrument, 'High', active_df, aadj_factor_df)
adj_low_df = FutData().load_act_adj_fut_data(instrument, 'Low', active_df, aadj_factor_df)
adj_return_df = adj_close_df / adj_close_df.shift(1) - 1
month_signal = pd.DataFrame(adj_return_df.index.strftime('%m'), index=adj_return_df.index, columns=['month'])
for n in window_list:
raw_factor = FutIndex.boll_fun(adj_close_df, n)
for limit in limit_list:
signal = Signal.fun_1(raw_factor, limit)
pos = Position.fun_1(signal)
pnl_df = (pos.shift(2) * adj_return_df).fillna(0)
sp = bt.AZ_Sharpe_y(pnl_df)
pot = bt.AZ_Pot(pos, pnl_df.sum())
print(instrument, sp, pot, n, limit)
if abs(sp) > 1.4:
print('!!!!!!!!!!!!!!!!')
print(f'sum:', sum(pnl_df))
if abs(sp) > 0.7:
for x, y in month_signal.groupby(by=['month']):
print(x, round(sum(pnl_df.loc[y.index]), 5))
plt.plot(pnl_df.cumsum())
def part_test(
con_id,
begin_time,
end_time,
# p_window, p_limit,
# v_window, v_limit,
# voli_window, voli_limit,
CCI_window,
CCI_limit,
# hold_time
):
try:
# print(con_id, begin_time, end_time)
data_df = fut_data.load_intra_data(con_id,
['High', 'Low', 'Close', 'Volume'])
# begin_time = pd.to_datetime('20190101')
# # end_time = begin_time + timedelta(1)
# end_time = pd.to_datetime('20190401')
# p_window = 1
# p_limit = 1
# v_window = 20
# v_limit = 2
part_data_df = data_df.truncate(before=begin_time, after=end_time)
part_data_df['weekday'] = pd.to_datetime(
part_data_df['Date']).dt.weekday
part_data_df['weekday_pos'] = (
(part_data_df['weekday'] != 2)
& (part_data_df['weekday'] != 1)
# & (part_data_df['weekday'] != 3)
# (part_data_df['weekday'] != 4)
).astype(int)
part_data_df['month'] = part_data_df['Date'].str.slice(5, 7)
part_data_df['month_pos'] = (
(part_data_df['month'] != '10') &
(part_data_df['month'] != '12')).astype(int)
# part_data_df['time_pos_cut'] = part_data_df.apply(lambda x: -1 if (((x['Time'] > '14:51')
# & (x['Time'] <= '15:00')
# # | (x['Time'] > '21:00')
# # & (x['Time'] <= '22:00')
# # | (x['Time'] > '21:00')
# # & (x['Time'] <= '22:00')
# )) else 1, axis=1)
v_window = 60
part_data_df['Volume_zscore'] = bt.AZ_Col_zscore(
part_data_df[['Volume']], v_window)
part_data_df['Volume_signal'] = (part_data_df['Volume_zscore'] <
2).astype(int).replace(0, -1)
p_window = 3
# part_data_df['past_min_pct_change'] = (part_data_df['Close'] - part_data_df['Close'].shift(p_window)) \
# / part_data_df['Close'].shift(p_window)
part_data_df['past_min_pct_change'] = (
part_data_df['Close'] - part_data_df['Close'].shift(p_window))
part_data_df['past_min_pct_signal'] = part_data_df['past_min_pct_change'] \
.apply(lambda x: 0 if abs(x) < 0.005 else (1 if x > 0.005 else -1))
part_data_df['CCI_score'] = FutIndex.CCI_fun(part_data_df['High'],
part_data_df['Low'],
part_data_df['Close'],
CCI_window)
part_data_df['CCI_signal'] = Signal.fun_1(part_data_df['CCI_score'],
CCI_limit)
part_data_df['CCI_pos'] = Position.fun_1(part_data_df['CCI_signal'])
part_data_df['boll_score'] = FutIndex.boll_fun(part_data_df['Close'],
CCI_window)
part_data_df['boll_signal'] = Signal.fun_1(part_data_df['boll_score'],
CCI_limit)
part_data_df['boll_pos'] = Position.fun_1(part_data_df['boll_signal'])
part_data_df['trend_signal'] = bt.AZ_Rolling(part_data_df['Close'],
500).std()
part_data_df['trend_pos'] = (part_data_df['trend_signal'] <
20).astype(int)
# part_data_df['position'] = part_data_df['boll_pos'] * part_data_df['weekday_pos'] * part_data_df['month_pos']
part_data_df[
'position'] = part_data_df['boll_pos'] * part_data_df['trend_pos']
part_data_df['position_sft'] = (part_data_df['position'].shift(2) *
part_data_df['weekday_pos']).fillna(0)
part_data_df['price_return'] = part_data_df['Close'] - part_data_df[
'Close'].shift(1)
# part_data_df['price_return_sum'] = bt.AZ_Rolling(part_data_df['price_return'], hold_time).sum() \
# .shift(-hold_time + 1)
part_data_df['pnl'] = part_data_df['position_sft'] * part_data_df[
'price_return']
part_data_df['asset'] = part_data_df['pnl'].cumsum()
# part_data_df['pnl_test'] = part_data_df['signal'] * part_data_df['price_return_sum'].shift(-2)
part_data_df['turnover'] = (part_data_df['position_sft'] - part_data_df['position_sft'].shift(1)) \
* part_data_df['Close']
# 剔除开盘收盘5min的signal
# plt.figure(figsize=[64, 64])
# ax1 = plt.subplot(4, 1, 1)
# ax2 = plt.subplot(4, 1, 2)
# ax3 = plt.subplot(4, 1, 3)
# ax1.plot(part_data_df['asset'].values)
# # ax2.plot(part_data_df['Close'].values, '--', color='#75bbfd')
# ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['position_sft'] > 0)],
# part_data_df['Close'][part_data_df['position_sft'] > 0], s=0.5, color='red')
#
# ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['position_sft'] == 0)],
# part_data_df['Close'][part_data_df['position_sft'] == 0], s=0.5, color='black')
#
# ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['position_sft'] < 0)],
# part_data_df['Close'][part_data_df['position_sft'] < 0], s=0.5, color='blue')
# # ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['past_min_pct_signal'] > 0)],
# # part_data_df['Close'][part_data_df['past_min_pct_signal'] > 0], s=20, color='black')
# # ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['past_min_pct_signal'] < 0)],
# # part_data_df['Close'][part_data_df['past_min_pct_signal'] < 0], s=20, color='y')
#
# ax3.bar(range(len(part_data_df.index)), part_data_df['Volume'].values)
#
# ax1.grid()
# ax2.grid()
# ax3.grid()
# plt.title(con_id)
# savfig_send(con_id)
#
part_pnl_df = part_data_df.groupby('Date')['pnl'].sum()
part_turnover = part_data_df.groupby('Date')['turnover'].apply(
lambda x: sum(abs(x)))
return part_pnl_df, part_turnover, part_data_df
except Exception as error:
print(error)
return pd.Series(), pd.Series(), pd.Series()
def part_test(
con_id,
begin_time,
end_time,
# p_window, p_limit,
# v_window, v_limit,
# voli_window, voli_limit,
CCI_window,
CCI_limit,
# hold_time,
cut_num):
# print(con_id, begin_time, end_time)
try:
# print(con_id, begin_time, end_time)
all_min_df = fut_data.load_intra_data(con_id, ['Close'])
data_df = fut_data.load_intra_reshape_data(
con_id, ['High', 'Low', 'Close', 'Volume', 'OpenInterest'],
cut_num=cut_num)
if con_id == 'J1501.DCE':
print(1)
# begin_time = pd.to_datetime('20190101')
# # end_time = begin_time + timedelta(1)
# end_time = pd.to_datetime('20190401')
# p_window = 1
# p_limit = 1
# v_window = 20
# v_limit = 2
part_data_df = data_df.truncate(before=begin_time, after=end_time)
part_data_df['OpenInterest_core'] = FutIndex.boll_fun(
data_df['OpenInterest'], 100)
part_data_df['weekday'] = pd.to_datetime(
part_data_df['Date']).dt.weekday
part_data_df['weekday_pos'] = (
(part_data_df['weekday'] != 3)
# & (part_data_df['weekday'] != 3)
# & (part_data_df['weekday'] != 3)
# (part_data_df['weekday'] != 4)
).astype(int)
part_data_df['month'] = part_data_df['Date'].str.slice(5, 7)
# part_data_df['month_pos'] = ((part_data_df['month'] != '10') & (part_data_df['month'] != '12')).astype(int)
part_data_df['month_pos'] = (part_data_df['month'] != '1').astype(int)
# part_data_df['time_pos_cut'] = part_data_df.apply(lambda x: -1 if (((x['Time'] > '14:51')
# & (x['Time'] <= '15:00')
# # | (x['Time'] > '21:00')
# # & (x['Time'] <= '22:00')
# # | (x['Time'] > '21:00')
# # & (x['Time'] <= '22:00')
# )) else 1, axis=1)
part_data_df[
'Vol_OI'] = part_data_df['Volume'] / part_data_df['OpenInterest']
part_data_df['Volume_boll'] = FutIndex.boll_fun(
part_data_df[['Volume']], CCI_window)
part_data_df['past_roll_Volume'] = bt.AZ_Rolling_mean(
part_data_df[['Volume']], CCI_window)
v_window = 60
part_data_df['Volume_zscore'] = bt.AZ_Col_zscore(
part_data_df[['Volume']], CCI_window)
part_data_df['Volume_signal'] = Signal.fun_1(
part_data_df['Volume_zscore'], 0.01)
# part_data_df['Volume_signal'] = (part_data_df['Volume_zscore'] < 2).astype(int).replace(0, -1)
p_window = 5
# part_data_df['past_min_pct_change'] = (part_data_df['Close'] - part_data_df['Close'].shift(p_window)) \
# / part_data_df['Close'].shift(p_window)
part_data_df['past_min_pct_change'] = (
part_data_df['Close'] / part_data_df['Close'].shift(p_window) - 1)
part_data_df['past_min_pct_signal'] = part_data_df['past_min_pct_change'] \
.apply(lambda x: 0 if abs(x) < 0.004 else (1 if x > 0.004 else -1))
test_window = CCI_window
macd, macdsignal, macdhist = ta.MACD(part_data_df['Close'], 12, 26, 9)
part_data_df['macd'] = macd
RSI = ta.RSI(part_data_df['Close'], test_window)
RSI = RSI - 50
RSI[RSI > 20] = 20
RSI[RSI < -20] = -20
part_data_df['RSI'] = RSI
part_data_df['RSI_signal'] = Signal.fun_1(part_data_df['RSI'], 1)
part_data_df['RSI_pos'] = Position.fun_1(part_data_df['RSI_signal'],
limit=60)
# aroondown, aroonup = ta.AROON(part_data_df['High'], part_data_df['Low'], test_window)
obv = ta.OBV(part_data_df['Close'], part_data_df['Volume'])
part_data_df['obv'] = obv
atr = ta.ATR(part_data_df['High'], part_data_df['Low'],
part_data_df['Close'], test_window)
part_data_df['atr'] = atr
adx = ta.ADX(part_data_df['High'], part_data_df['Low'],
part_data_df['Close'], test_window)
part_data_df['adx'] = adx
trix = ta.TRIX(part_data_df['Close'], 60)
part_data_df['trix'] = trix
willr = ta.WILLR(part_data_df['High'], part_data_df['Low'],
part_data_df['Close'], test_window)
part_data_df['willr'] = willr
part_data_df['CCI_score'] = FutIndex.CCI_fun(part_data_df['High'],
part_data_df['Low'],
part_data_df['Close'],
CCI_window)
part_data_df['CCI_signal'] = Signal.fun_1(part_data_df['CCI_score'],
CCI_limit)
part_data_df['CCI_pos'] = Position.fun_1(part_data_df['CCI_signal'])
part_data_df['boll_score'], ma_n, md_n = FutIndex.boll_fun(
part_data_df['Close'], CCI_window, return_line=True)
part_data_df['boll_signal'] = Signal.fun_1(part_data_df['boll_score'],
CCI_limit)
part_data_df['boll_pos'] = Position.fun_1(part_data_df['boll_signal'])
# part_data_df['boll_pos'] = Position.fun_3(part_data_df['boll_signal'], part_data_df['past_min_pct_signal'])
part_data_df['test_score'] = FutIndex.test_fun(part_data_df['Close'],
CCI_window,
cap=5,
num=3,
return_line=False)
part_data_df['test_signal'] = Signal.fun_1(part_data_df['test_score'],
CCI_limit)
part_data_df['test_pos'] = Position.fun_1(part_data_df['test_signal'])
# part_data_df['trend_signal'] = bt.AZ_Rolling(part_data_df['Close'], 500).std()
# part_data_df['trend_pos'] = (part_data_df['trend_signal'] < 40).astype(int)
part_data_df['trend_indicator'] = bt.AZ_Rolling(part_data_df['Close'], 100). \
apply(lambda x: (x[-1] / x[0] - 1) / (max(x) / min(x) - 1), raw=False)
part_data_df['trend_pos'] = (part_data_df['trend_indicator'] <
0.5).astype(int)
# part_data_df['position'] = part_data_df['boll_pos'] * part_data_df['weekday_pos'] * part_data_df['month_pos']
# part_data_df['position'] = part_data_df['CCI_pos']
part_data_df['position'] = part_data_df['CCI_pos']
part_data_df['position_exe'] = part_data_df['position'].shift(1)
part_data_df['position_sft'] = part_data_df['position'].shift(2)
# part_data_df['price_return'] = part_data_df['Close'] - part_data_df['Close'].shift(1)
part_data_df['next_trade_close'] = all_min_df['Close'].shift(
-2).reindex(part_data_df.index)
part_data_df['price_return'] = part_data_df['next_trade_close'] - \
part_data_df['next_trade_close'].shift(1)
# part_data_df['price_return_sum'] = bt.AZ_Rolling(part_data_df['price_return'], hold_time).sum() \
# .shift(-hold_time + 1)
# part_data_df['pnl'] = part_data_df['position_sft'] * part_data_df['price_return']
part_data_df['pnl'] = part_data_df['position_exe'] * part_data_df[
'price_return']
part_data_df['asset'] = part_data_df['pnl'].cumsum()
# part_data_df['pnl_test'] = part_data_df['signal'] * part_data_df['price_return_sum'].shift(-2)
part_data_df['turnover'] = (part_data_df['position_sft'] - part_data_df['position_sft'].shift(1)) \
* part_data_df['Close']
# 剔除开盘收盘5min的signal
# if '1910' in con_id:
# plt.figure(figsize=[64, 64])
# ax1 = plt.subplot(4, 1, 1)
# ax2 = plt.subplot(4, 1, 2)
# ax3 = plt.subplot(4, 1, 3)
# ax1.plot(part_data_df['asset'].values)
# # ax2.plot(part_data_df['Close'].values, '--', color='#75bbfd')
# ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['position_sft'] > 0)],
# part_data_df['Close'][part_data_df['position_sft'] > 0], s=0.5, color='red')
#
# ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['position_sft'] == 0)],
# part_data_df['Close'][part_data_df['position_sft'] == 0], s=0.5, color='black')
#
# ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['position_sft'] < 0)],
# part_data_df['Close'][part_data_df['position_sft'] < 0], s=0.5, color='blue')
#
# # ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['past_min_pct_signal'] > 0)],
# # part_data_df['Close'][part_data_df['past_min_pct_signal'] > 0], s=20, color='y')
# # ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['past_min_pct_signal'] < 0)],
# # part_data_df['Close'][part_data_df['past_min_pct_signal'] < 0], s=20, color='#f504c9')
#
# ax3.bar(range(len(part_data_df.index)), part_data_df['Volume'].values)
#
# ax1.grid()
# ax2.grid()
# ax3.grid()
# plt.title(con_id)
# savfig_send(con_id)
part_pnl_df = part_data_df.groupby('Date')['pnl'].sum()
part_turnover = part_data_df.groupby('Date')['turnover'].apply(
lambda x: sum(abs(x)))
return part_pnl_df, part_turnover, part_data_df
except Exception as error:
print(error)
return pd.Series(), pd.Series(), pd.Series()
def part_test(con_id, begin_time, end_time, CCI_window, CCI_limit):
try:
data_df = fut_data.load_intra_data(con_id,
['High', 'Low', 'Close', 'Volume'])
part_data_df = data_df.truncate(before=begin_time, after=end_time)
part_data_df['weekday'] = pd.to_datetime(
part_data_df['Date']).dt.weekday
part_data_df['weekday_pos'] = (
(part_data_df['weekday'] != 2)
& (part_data_df['weekday'] != 1)
# & (part_data_df['weekday'] != 3)
# (part_data_df['weekday'] != 4)
).astype(int).shift(-2)
part_data_df['boll_score'] = FutIndex.boll_fun(part_data_df['Close'],
CCI_window)
part_data_df['boll_signal'] = Signal.fun_1(part_data_df['boll_score'],
CCI_limit)
part_data_df['boll_pos'] = Position.fun_1(part_data_df['boll_signal'])
part_data_df['trend_signal'] = bt.AZ_Rolling(part_data_df['Close'],
500).std()
part_data_df['trend_pos'] = (part_data_df['trend_signal'] <
20).astype(int)
part_data_df['position'] = part_data_df['boll_pos'] * part_data_df[
'trend_pos'] * part_data_df['weekday_pos']
part_data_df['position_exe'] = part_data_df['position'].shift(
1).fillna(0)
part_data_df['position_sft'] = part_data_df['position'].shift(
2).fillna(0)
part_data_df['price_return'] = part_data_df['Close'] / part_data_df[
'Close'].shift(1) - 1
part_data_df['pnl'] = part_data_df['position_sft'] * part_data_df[
'price_return']
part_data_df['asset'] = part_data_df['pnl'].cumsum()
part_data_df['turnover'] = (part_data_df['position_sft'] -
part_data_df['position_sft'].shift(1))
part_data_df['con_id'] = con_id
part_pnl_df = part_data_df.groupby('Date')['pnl'].sum()
part_turnover = part_data_df.groupby('Date')['turnover'].apply(
lambda x: sum(abs(x)))
# 剔除开盘收盘5min的signal
plt.figure(figsize=[64, 64])
ax1 = plt.subplot(4, 1, 1)
ax2 = plt.subplot(4, 1, 2)
ax3 = plt.subplot(4, 1, 3)
ax1.plot(part_data_df['asset'].values)
# ax2.plot(part_data_df['Close'].values, '--', color='#75bbfd')
ax2.scatter(np.array(range(len(
part_data_df.index)))[(part_data_df['position_sft'] > 0)],
part_data_df['Close'][part_data_df['position_sft'] > 0],
s=0.5,
color='red')
ax2.scatter(np.array(range(len(
part_data_df.index)))[(part_data_df['position_sft'] == 0)],
part_data_df['Close'][part_data_df['position_sft'] == 0],
s=0.5,
color='black')
ax2.scatter(np.array(range(len(
part_data_df.index)))[(part_data_df['position_sft'] < 0)],
part_data_df['Close'][part_data_df['position_sft'] < 0],
s=0.5,
color='blue')
# ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['past_min_pct_signal'] > 0)],
# part_data_df['Close'][part_data_df['past_min_pct_signal'] > 0], s=20, color='black')
# ax2.scatter(np.array(range(len(part_data_df.index)))[(part_data_df['past_min_pct_signal'] < 0)],
# part_data_df['Close'][part_data_df['past_min_pct_signal'] < 0], s=20, color='y')
ax3.bar(range(len(part_data_df.index)), part_data_df['Volume'].values)
ax1.grid()
ax2.grid()
ax3.grid()
plt.title(con_id)
savfig_send(con_id)
return part_pnl_df, part_turnover, part_data_df
except Exception as error:
print(error)
return None