def analyzePriceFollow(target_date, stock_id, is_index, threshold):
file_postfix = 'Timing_%s_%s' % (u.stockFileName(stock_id,
is_index), threshold)
timing = u.read_csv(c.path_dict['strategy'] + file_postfix + '.csv',
encoding='gbk')
timing_number = len(timing)
# Find the matched timing date and trend
timing_index = -1
for i in range(timing_number):
date = dt.datetime.strptime(timing.ix[i, 'date'], '%Y-%m-%d').date()
if date <= target_date:
timing_index = i
else:
break
# Report results
if timing_index != -1:
date = dt.datetime.strptime(timing.ix[timing_index, 'date'],
'%Y-%m-%d').date()
trend = timing.ix[timing_index, 'trend']
if date == target_date: # Given target_date is Timing Date
print('Date', target_date, ': Trend of',
u.stockFileName(stock_id, is_index), 'Goes', trend)
else:
print('Date', target_date, ': Trend of',
u.stockFileName(stock_id, is_index),
'Does Not Change, Still', trend)
else:
print('Date', target_date, ': Trend of',
u.stockFileName(stock_id, is_index),
'Not Available, No Timing Data')
def loadDailyHFQ(stock_id, is_index):
fullpath = c.fullpath_dict['lshq'] % u.stockFileName(stock_id, is_index)
# Ensure data file is available
if not u.hasFile(fullpath):
print('Require LSHQ of %s!' % u.stockFileName(stock_id, is_index))
return None
# Read data file
df = u.read_csv(fullpath)
return df
def lshqCompare():
# Check pre-requisite
basics = loadStockBasics()
if u.isNoneOrEmpty(basics):
print('Need to have stock basics!')
raise SystemExit
# Iterate over all stocks
stocks_number = len(basics)
unmatched = []
for i in range(stocks_number):
stock_id = u.stockID(basics.loc[i,'code'])
file = c.file_trading_lshq % u.stockFileName(stock_id, False)
if not fileCompare(src_path + file, tar_path + file):
unmatched.append(stock_id)
# Output Unmatched
print(unmatched)
def getDailyHFQ(stock_id, is_index, date_start, date_end, time_to_market, incremental):
hfq = None
# For incremental update, use the next day of last update as the date start.
if incremental:
hfq = loadDailyHFQ(stock_id, is_index)
if not u.isNoneOrEmpty(hfq):
hfq.set_index('date',inplace=True)
hfq.sort_index(ascending=True,inplace=True)
last_day = hfq.index[len(hfq)-1]
date_start = u.nextDayFromStr(last_day)
# Check if existing data file is already up-to-date
if date_end < date_start:
return
# Download
if gs.is_debug:
print('Download Daily HFQ: %s, start=%s, end=%s' % (stock_id, u.dateToStr(date_start), u.dateToStr(date_end)))
df = get_daily_hfq(stock_id=stock_id, is_index=is_index, date_start=date_start,
date_end=date_end, time_to_market=time_to_market)
if not u.isNoneOrEmpty(df):
# Default df has 'date' set as index with type pandas.tslib.Timestamp,
# so merged results will contain 00:00:00 along with date.
# With below series of operation, index of df will be converted to type
# str, thus avoid above issue.
df.reset_index(inplace=True)
df['date'] = df['date'].map(lambda x:str(x.date()))
df.set_index('date',inplace=True)
df.sort_index(ascending=True,inplace=True)
# For incremental update, merge data
if incremental:
if (not u.isNoneOrEmpty(df)) and (not u.isNoneOrEmpty(hfq)):
df = hfq.append(df)
# Format Columns
if not u.isNoneOrEmpty(df):
for column in ['open', 'high', 'close', 'low', 'volume', 'amount', 'factor']:
df[column] = df[column].map(lambda x:'%.3f' % float(x))
# Save to CSV File
if not u.isNoneOrEmpty(df):
u.to_csv(df, c.path_dict['lshq'], c.file_dict['lshq'] % u.stockFileName(stock_id, is_index))
def strategyAncleXu(stock_id, is_index):
# Load Stock Daily QFQ Data
lshq = loadDailyQFQ(stock_id, is_index)
if u.isNoneOrEmpty(lshq):
raise SystemExit
# Calculate Long Range High/Low
lshq['range_high_long'] = 0.0
lshq['range_low_long'] = 0.0
lshq_number = len(lshq)
for i in range(lshq_number):
index_beg = (i - bar_range_long) if (i - bar_range_long) > 0 else 0
index_end = i if i > 0 else 1
lshq.ix[i,
'range_high_long'] = np.max(lshq['high'][index_beg:index_end])
lshq.ix[i, 'range_low_long'] = np.min(lshq['low'][index_beg:index_end])
# Calculate Short Range High/Low
lshq['range_high_short'] = 0.0
lshq['range_low_short'] = 0.0
for i in range(lshq_number):
index_beg = (i - bar_range_short) if (i - bar_range_short) > 0 else 0
index_end = i if i > 0 else 1
lshq.ix[i,
'range_high_short'] = np.max(lshq['high'][index_beg:index_end])
lshq.ix[i,
'range_low_short'] = np.min(lshq['low'][index_beg:index_end])
# Calculate R and Avg(R)
lshq['R'] = 0.0
lshq['Avg_R'] = 0.0
for i in range(lshq_number):
prev_close = lshq.ix[i - 1, 'close'] if i > 0 else lshq.ix[i, 'open']
high = lshq.ix[i, 'high']
low = lshq.ix[i, 'low']
lshq.ix[i, 'R'] = np.max([
np.abs(high - low),
np.abs(high - prev_close),
np.abs(low - prev_close)
])
index_beg = (i - r_range) if (i - r_range) > 0 else 0
index_end = i if i > 0 else 1
lshq.ix[i, 'Avg_R'] = np.mean(lshq['R'][index_beg:index_end])
# Save to CSV File
file_postfix = 'AncleXu_%s' % u.stockFileName(stock_id, is_index)
u.to_csv(lshq, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
# Run Strategy
lshq['long_open'] = False
lshq['long_close'] = False
lshq['short_open'] = False
lshq['short_close'] = False
has_long_open = False
has_short_open = False
event_index = []
for i in range(lshq_number):
close = lshq.ix[i, 'close']
range_high_long = lshq.ix[i, 'range_high_long']
range_low_long = lshq.ix[i, 'range_low_long']
range_high_short = lshq.ix[i, 'range_high_short']
range_low_short = lshq.ix[i, 'range_low_short']
has_event = False
if has_long_open == False:
if close > range_high_long: # Long Open
lshq.ix[i, 'long_open'] = True
has_long_open = True
has_event = True
else:
if close < range_low_short:
lshq.ix[i, 'long_close'] = True
has_long_open = False
has_event = True
if has_short_open == False:
if close < range_low_long: # Short Open
lshq.ix[i, 'short_open'] = True
has_short_open = True
has_event = True
else:
if close > range_high_short:
lshq.ix[i, 'short_close'] = True
has_short_open = False
has_event = True
if has_event:
event_index.append(i)
# Strategy Statistics
stats = lshq.ix[event_index, [
'date', 'close', 'long_open', 'long_close', 'short_open', 'short_close'
]]
if gs.is_debug:
print(stats.head(10))
stats_number = len(stats)
long_number = 0
short_number = 0
long_open_date = []
long_open_price = []
long_close_date = []
long_close_price = []
short_open_date = []
short_open_price = []
short_close_date = []
short_close_price = []
for i in range(stats_number):
index = stats.index[i]
print('Type is:', type(stats.ix[index, 'long_open']))
if stats.ix[index, 'long_open'] == True: # Long Open
long_open_date.append(stats.ix[index, 'date'])
long_open_price.append(stats.ix[index, 'close'])
if stats.ix[index, 'long_close'] == True: # Long Close
long_close_date.append(stats.ix[index, 'date'])
long_close_price.append(stats.ix[index, 'close'])
long_number = long_number + 1
if stats.ix[index, 'short_open'] == True: # Short Open
short_open_date.append(stats.ix[index, 'date'])
short_open_price.append(stats.ix[index, 'close'])
if stats.ix[index, 'short_close'] == True: # Short Close
short_close_date.append(stats.ix[index, 'date'])
short_close_price.append(stats.ix[index, 'close'])
short_number = short_number + 1
# Profit Statistics
long_profit = []
total_long_profit = 0.0
for i in range(long_number):
profit = long_close_price[i] - long_open_price[i]
long_profit.append(profit)
total_long_profit = total_long_profit + profit
short_profit = []
total_short_profit = 0.0
for i in range(short_number):
profit = short_open_price[i] - short_close_price[i]
short_profit.append(profit)
total_short_profit = total_short_profit + profit
print('\nStrategy Complete:')
print('Total Long Trading = %04d, Total Long Profit = %.2f' %
(long_number, total_long_profit))
print('Total Short Trading = %04d, Total Short Profit = %.2f' %
(short_number, total_short_profit))
print('Total Trading = %04d, Total Profit = %.2f' %
(long_number + short_number, total_long_profit + total_short_profit))
for i in range(long_number):
print(
' B%04d, Open (%s) = %4.2f, Close (%s) = %4.2f, Profit = %4.2f' %
(i + 1, long_open_date[i], long_open_price[i], long_close_date[i],
long_close_price[i], long_profit[i]))
print(
' -------------------------------------------------------------------'
)
for i in range(short_number):
print(
' S%04d, Open (%s) = %4.2f, Close (%s) = %4.2f, Profit = %4.2f' %
(i + 1, short_open_date[i], short_open_price[i],
short_close_date[i], short_close_price[i], short_profit[i]))
# Trading Dataframe
data_index_number = long_number + short_number
data_columns = [
'type', 'open_date', 'open_price', 'close_date', 'close_price',
'profit'
]
data_columns_number = len(data_columns)
data_init = np.random.randn(data_index_number * data_columns_number)
for i in range(data_index_number * data_columns_number):
data_init[i] = np.nan
data_index = []
for i in range(data_index_number):
data_index.append(i)
trading = pd.DataFrame(data_init.reshape(data_index_number,
data_columns_number),
index=data_index,
columns=data_columns)
for i in range(long_number):
trading.ix[i, 'type'] = 'B%04d' % (i + 1)
trading.ix[i, 'open_date'] = long_open_date[i]
trading.ix[i, 'open_price'] = long_open_price[i]
trading.ix[i, 'close_date'] = long_close_date[i]
trading.ix[i, 'close_price'] = long_close_price[i]
trading.ix[i, 'profit'] = long_profit[i]
for i in range(short_number):
trading.ix[i + long_number, 'type'] = 'S%04d' % (i + 1)
trading.ix[i + long_number, 'open_date'] = short_open_date[i]
trading.ix[i + long_number, 'open_price'] = short_open_price[i]
trading.ix[i + long_number, 'close_date'] = short_close_date[i]
trading.ix[i + long_number, 'close_price'] = short_close_price[i]
trading.ix[i + long_number, 'profit'] = short_profit[i]
# Save to CSV File
u.to_csv(trading, c.path_dict['strategy'],
c.file_dict['strategy_r'] % file_postfix)
def optimizePriceFollow(stock_list, is_index, threshold_list, method):
stock_number = len(stock_list)
if stock_number < 1:
print('Stock Number:', stock_number)
raise SystemExit
threshold_number = len(threshold_list)
if threshold_number < 1:
print('Threshold Number:', threshold_number)
raise SystemExit
for stock_id in stock_list:
# Load Results from Different Threshold
file_postfix = 'PriceFollow_%s_All' % u.stockFileName(
stock_id, is_index)
df = u.read_csv(c.path_dict['strategy'] +
c.file_dict['strategy'] % file_postfix)
row_number = len(df)
for i in range(1, threshold_number - 1):
t_prev = threshold_list[i - 1]
t_curr = threshold_list[i]
t_next = threshold_list[i + 1]
t_total = t_prev + t_curr + t_next
column_postfix = '_%s' % t_curr
# Slice Interested Columns
select_columns = []
for t in [t_prev, t_curr, t_next]:
select_columns.append('predict' + '_%s' % t)
select_columns.append('confirm' + column_postfix)
df2 = pd.DataFrame.copy(df.loc[:, select_columns])
# Calculate Weighted Predict
df2['weighted_predict' + column_postfix] = np.nan
for j in range(1, row_number):
# Method 1: Threshold Weighted Predict
if method == 'Threshold Weighted':
predict = 0.0
for t in [t_prev, t_curr, t_next]:
predict = predict + t * df2.ix[j,
'predict' + '_%s' % t]
predict = predict / t_total
df2.ix[j, 'weighted_predict' + column_postfix] = predict
# Method 2: Equally Weighted Predict
elif method == 'Equally Weighted':
predict = 0.0
for t in [t_prev, t_curr, t_next]:
predict = predict + 1.0 * df2.ix[j,
'predict' + '_%s' % t]
predict = predict / 3.0
df2.ix[j, 'weighted_predict' + column_postfix] = predict
# Method 3: Single Predict
else:
predict = df2.ix[j, 'predict' + column_postfix]
df2.ix[j, 'weighted_predict' + column_postfix] = predict
# Optimize for Given Segments within Range [1, -1] to Find Best Cutoff
print('Optimization Starts for Threshold:', t_curr)
segments = 10
delta_mean = []
delta_stddev = []
for j in range(1, row_number):
weighted_predict = df2.ix[j,
'weighted_predict' + column_postfix]
confirm = df2.ix[j, 'confirm' + column_postfix]
for k in range(1, segments):
ratio = float(k) / float(segments)
cutoff = 1.0 * (1.0 - ratio) + (-1.0) * ratio
predict_cutoff = 1 if weighted_predict > cutoff else -1
delta_cutoff = predict_cutoff - confirm if not np.isnan(
confirm) else np.nan
df2.ix[j, 'delta' + '_%.2f' % cutoff] = delta_cutoff
# Save to CSV File
file_postfix = 'PriceFollow_%s_Cutoff_%s' % (u.stockFileName(
stock_id, is_index), t_curr)
u.to_csv(df2, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
# Gather Mean of Delta_Cutoffs
for k in range(1, segments):
ratio = float(k) / float(segments)
cutoff = 1.0 * (1.0 - ratio) + (-1.0) * ratio
describe = df2['delta' + '_%.2f' % cutoff].describe()
delta_mean.append(np.abs(describe['mean']))
delta_stddev.append(describe['std'])
# Find Best Cutoff
delta_mean_min = min(delta_mean)
delta_mean_index = delta_mean.index(delta_mean_min)
print('Delta Mean:', delta_mean)
print('Delta Stddev:', delta_stddev)
print('Delta Mean Min:', delta_mean_min)
print('Delta Mean Index:', delta_mean_index)
ratio = float(delta_mean_index + 1) / float(segments)
best_cutoff = 1.0 * (1.0 - ratio) + (-1.0) * ratio
print('Best Cutoff:', best_cutoff)
print('Optimization Ends for Threshold:', t_curr)
def strategyPriceFollow(stock_id, is_index, trend_threshold):
# Load Stock Daily QFQ Data
lshq = loadDailyQFQ(stock_id, is_index)
if u.isNoneOrEmpty(lshq):
raise SystemExit
# Calculate Trend, Trend High, Trend Low, Trend Ref
lshq['trend'] = 'Up'
for column in ['trend_high', 'trend_low', 'trend_ref']:
lshq[column] = 0.0
for column in ['predict', 'confirm']:
lshq[column] = np.nan
lshq_number = len(lshq)
trends = []
trend_turning_points = []
trend_index_highs = []
trend_index_lows = []
index_high = 0
index_low = 0
for i in range(lshq_number):
if i == 0: # Initialization
lshq.ix[i, 'trend'] = 'Up'
trends.append('Up')
trend_turning_points.append(i)
for column in ['trend_high', 'trend_low', 'trend_ref']:
lshq.ix[i, column] = lshq.ix[i, 'close']
else:
trend = lshq.ix[i - 1, 'trend']
trend_high = lshq.ix[i - 1, 'trend_high']
trend_low = lshq.ix[i - 1, 'trend_low']
trend_ref = lshq.ix[i - 1, 'trend_ref']
trend_cur = lshq.ix[i, 'close']
up_to_down = False
down_to_up = False
if trend == 'Up':
if (1.0 - trend_cur / trend_high) > trend_threshold:
lshq.ix[i, 'trend'] = 'Down'
up_to_down = True
trends.append('Down')
trend_turning_points.append(i)
trend_index_highs.append(index_high)
else:
lshq.ix[i, 'trend'] = 'Up'
up_to_down = False
else:
if (trend_cur / trend_low - 1.0) > trend_threshold:
lshq.ix[i, 'trend'] = 'Up'
down_to_up = True
trends.append('Up')
trend_turning_points.append(i)
trend_index_lows.append(index_low)
else:
lshq.ix[i, 'trend'] = 'Down'
down_to_up = False
if trend == 'Up':
if up_to_down == False: # Up trend continues
if trend_cur > trend_high:
lshq.ix[i, 'predict'] = 1.0
for j in range(
index_high + 1, i + 1
): # New high confirms all trades since last high to be up-trend
lshq.ix[j, 'confirm'] = 1.0
lshq.ix[i, 'trend_high'] = trend_cur
index_high = i
else:
ratio = (1.0 -
trend_cur / trend_high) / trend_threshold
lshq.ix[i, 'predict'] = 1.0 * (1.0 - ratio) + (
-1.0) * ratio # Map to [1.0, -1.0]
lshq.ix[i, 'trend_high'] = trend_high
lshq.ix[i, 'trend_ref'] = trend_ref
else: # Up trend reverses
lshq.ix[i, 'predict'] = -1.0
for j in range(
index_high + 1, i + 1
): # Turning point confirms all trades since last high to be down-trend
lshq.ix[j, 'confirm'] = -1.0
lshq.ix[i, 'trend_ref'] = trend_high
lshq.ix[i, 'trend_low'] = trend_cur
index_low = i
else:
if down_to_up == False: # Down trend continues
if trend_cur < trend_low:
lshq.ix[i, 'predict'] = -1.0
for j in range(
index_low + 1, i + 1
): # New low confirms all trades since last low to be down-trend
lshq.ix[j, 'confirm'] = -1.0
lshq.ix[i, 'trend_low'] = trend_cur
index_low = i
else:
ratio = (trend_cur / trend_low - 1.0) / trend_threshold
lshq.ix[i, 'predict'] = (-1.0) * (1.0 - ratio) + (
1.0) * ratio # Map to [1.0, -1.0]
lshq.ix[i, 'trend_low'] = trend_low
lshq.ix[i, 'trend_ref'] = trend_ref
else: # Down trend reverses
lshq.ix[i, 'predict'] = 1.0
for j in range(
index_low + 1, i + 1
): # Turning point confirms all trades since last low to be up-trend
lshq.ix[j, 'confirm'] = 1.0
lshq.ix[i, 'trend_ref'] = trend_low
lshq.ix[i, 'trend_high'] = trend_cur
index_high = i
# Handle Last Trend
if i == lshq_number - 1:
if lshq.ix[i, 'trend'] == 'Up':
trend_index_highs.append(i)
else:
trend_index_lows.append(i)
# Calculate Trend Price
lshq['trend_price'] = 0.0
trend_number = len(trends)
print('Trend # =', trend_number)
index_ref = 0
index_tar = 0
price_ref = 0.0
price_tar = 0.0
idx_high = 0
idx_low = 0
for i in range(trend_number):
trend = trends[i]
index_tar = trend_index_highs[
idx_high] if trend == 'Up' else trend_index_lows[idx_low]
price_ref = lshq.ix[index_ref, 'close']
price_tar = lshq.ix[index_tar, 'close']
for index in range(index_ref, index_tar):
ratio = float(index - index_ref) / float(index_tar - index_ref)
lshq.ix[
index,
'trend_price'] = price_ref * (1.0 - ratio) + price_tar * ratio
if trend == 'Up':
index_ref = trend_index_highs[idx_high]
idx_high = idx_high + 1
else:
index_ref = trend_index_lows[idx_low]
idx_low = idx_low + 1
# Handle Last Trend
if i == trend_number - 1:
lshq.ix[index_tar, 'trend_price'] = price_tar
# Record Timing Data
trend_number = len(trends)
timing = u.createDataFrame(trend_number, ['date', 'trend'])
for i in range(trend_number):
trend = trends[i]
index = trend_turning_points[i]
timing.ix[i, 'date'] = lshq.ix[index, 'date']
timing.ix[i, 'trend'] = trend
timing.set_index('date', inplace=True)
timing.sort_index(ascending=True, inplace=True)
# Save to CSV File
file_postfix = 'Timing_%s_%s' % (u.stockFileName(
stock_id, is_index), trend_threshold)
u.to_csv(timing,
c.path_dict['strategy'],
file_postfix + '.csv',
encoding='gbk')
# Format Data Frame
for column in ['trend_high', 'trend_low', 'trend_ref', 'trend_price']:
lshq[column] = lshq[column].map(lambda x: '%.3f' % x)
lshq[column] = lshq[column].astype(float)
lshq.set_index('date', inplace=True)
lshq.sort_index(ascending=True, inplace=True)
# Save to CSV File
file_postfix = 'PriceFollow_%s_%s' % (u.stockFileName(
stock_id, is_index), trend_threshold)
u.to_csv(lshq, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
def mergePriceFollow(stock_list, is_index, threshold_list):
stock_number = len(stock_list)
if stock_number < 1:
print('Stock Number:', stock_number)
raise SystemExit
threshold_number = len(threshold_list)
if threshold_number < 1:
print('Threshold Number:', threshold_number)
raise SystemExit
# Init Price Follow Statistics for All Indexes
stats_columns = ['date', 'index']
for i in range(1, threshold_number - 1):
stats_columns.append('wpredict_%s' % threshold_list[i])
stats_columns.append('wtrend_%s' % threshold_list[i])
stats = u.createDataFrame(stock_number, stats_columns)
for s in range(stock_number):
stock_id = stock_list[s]
# Load Results from Different Threshold
dfs = []
for i in range(threshold_number):
threshold = threshold_list[i]
file_postfix = 'PriceFollow_%s_%s' % (u.stockFileName(
stock_id, is_index), threshold)
fullpath = c.path_dict[
'strategy'] + c.file_dict['strategy'] % file_postfix
df = u.read_csv(fullpath)
dfs.append(df)
# Compose Final Results
drop_columns = [
'trend', 'trend_high', 'trend_low', 'trend_ref', 'trend_price',
'predict', 'confirm'
]
df = dfs[0].drop(drop_columns, axis=1)
for i in range(threshold_number):
threshold = threshold_list[i]
column = 'trend'
df[column + '_%s' % threshold] = dfs[i][column]
for i in range(threshold_number):
threshold = threshold_list[i]
column = 'trend_price'
df[column + '_%s' % threshold] = dfs[i][column]
for i in range(threshold_number):
threshold = threshold_list[i]
column = 'predict'
df[column + '_%s' % threshold] = dfs[i][column]
for i in range(threshold_number):
threshold = threshold_list[i]
column = 'confirm'
df[column + '_%s' % threshold] = dfs[i][column]
# Weighted Predict Columns
cutoff = 0.0 # Optimized cutoff for weighted predict
for i in range(1, threshold_number - 1):
t_prev = threshold_list[i - 1]
t_curr = threshold_list[i]
t_next = threshold_list[i + 1]
t_total = t_prev + t_curr + t_next
column_postfix = '_%s' % t_curr
df['wpredict' + column_postfix] = np.nan
df['wtrend' + column_postfix] = np.nan
row_number = len(df)
for j in range(1, row_number):
wpredict = 0.0
for t in [t_prev, t_curr, t_next]:
wpredict = wpredict + t * df.ix[j, 'predict' + '_%s' % t]
wpredict = wpredict / t_total
df.ix[j, 'wpredict' + column_postfix] = wpredict
df.ix[j, 'wtrend' +
column_postfix] = 'Up' if wpredict >= cutoff else 'Down'
# Fill One Row of Statistics
last_index = len(df) - 1
stats.ix[s, 'date'] = df.ix[last_index, 'date']
stats.ix[s, 'index'] = stock_id
for i in range(1, threshold_number - 1):
column_postfix = '_%s' % threshold_list[i]
stats.ix[s, 'wpredict' +
column_postfix] = df.ix[last_index,
'wpredict' + column_postfix]
stats.ix[s,
'wtrend' + column_postfix] = df.ix[last_index, 'wtrend' +
column_postfix]
# Format Columns
df.set_index('date', inplace=True)
# Save to CSV File
file_postfix = 'PriceFollow_%s_All' % u.stockFileName(
stock_id, is_index)
u.to_csv(df, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
# Format Columns
stats.set_index('date', inplace=True)
# Save to CSV File
file_postfix = 'PriceFollow_Statistics'
u.to_csv(stats, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
def validDailyHFQ(stock_id, is_index, force_update):
if force_update == True:
return False
else:
return u.hasFile(c.fullpath_dict['lshq'] % u.stockFileName(stock_id, is_index))
def updateSamplePrice(benchmark_id, stock_ids, is_index, period):
'''
函数功能：
--------
根据基准指数的时间范围和采样周期，对指定股票/指数列表的收盘价格进行全采样。
输入参数：
--------
benchmark_id : string, 指数代码 e.g. '000300'，隐含起止时间。
period : string, 采样周期 e.g. 'M'，支持'D', 'W', and 'M'。
输出参数：
--------
采样成功时，allprice : pandas.Series，采样结果。
采样失败时，None
'''
# Load Benchmark
benchmark = loadDailyQFQ(benchmark_id, True)
if u.isNoneOrEmpty(benchmark):
print('Require Benchmark LSHQ File: %s!' %
u.stockFileName(benchmark_id, True))
return None
# Resample Benchmark
benchmark['date'] = benchmark['date'].astype(np.datetime64)
benchmark.set_index('date', inplace=True)
benchmark.sort_index(ascending=True, inplace=True)
if gs.is_debug:
print(benchmark.head(10))
drop_columns = ['open', 'high', 'low', 'volume', 'amount']
benchmark.drop(drop_columns, axis=1, inplace=True)
bench_resample = pd.DataFrame()
if period == 'D':
bench_resample = benchmark
else:
bench_resample = benchmark.resample(period).first()
bench_resample['close'] = benchmark['close'].resample(period).last()
# Resample daily data by weekly may introduce N/A price (due to holiday weeks)
# This does not exist for monthly resample (as no holiday month so far)
if period == 'W':
bench_resample.dropna(axis=0, how='any', inplace=True)
bench_resample['close'] = bench_resample['close'].map(lambda x: '%.3f' % x)
bench_resample['close'] = bench_resample['close'].astype(float)
stock_list = [bench_resample]
# Iterate over all stocks
stocks_number = len(stock_ids)
for i in range(stocks_number):
# Load Stock LSHQ
stock_id = u.stockID(stock_ids[i])
stock = loadDailyQFQ(stock_id, is_index)
if u.isNoneOrEmpty(stock):
print('Require Stock/Index LSHQ File: %s!' %
u.stockFileName(stock_id, is_index))
continue
stock['date'] = stock['date'].astype(np.datetime64)
stock.set_index('date', inplace=True)
stock.sort_index(ascending=True, inplace=True)
if gs.is_debug:
print(stock.head(10))
# Resample Stock LSHQ
stock.drop(drop_columns, axis=1, inplace=True)
if period == 'D':
stock_resample = stock
else:
stock_resample = stock.resample(period).first()
stock_resample['close'] = stock['close'].resample(period).last()
stock_resample['close'] = stock_resample['close'].map(
lambda x: '%.3f' % x)
stock_resample['close'] = stock_resample['close'].astype(float)
# Merge Benchmark with Stock
df = pd.merge(bench_resample,
stock_resample,
how='left',
left_index=True,
right_index=True,
sort=True,
suffixes=('', '_' + stock_id))
df.drop(['close'], axis=1, inplace=True)
stock_list.append(df)
# Merge Results
allprice = pd.concat(stock_list, axis=1, join='inner')
return allprice