def get_adjusted_ttm(test_report_date, year):
ind_ratio_map = cal_industry_ratio(test_report_date, year)
prev_report_dates = [
get_report_date(test_report_date, -i) for i in range(1, 9)
]
ret = pd.DataFrame()
for report_date in prev_report_dates:
report_date_dt = datetime.strptime(report_date, '%Y-%m-%d')
quarter = report_date_dt.month // 4 + 1
dict = ind_ratio_map[quarter - 1]['ratio'].to_dict()
# 读取revenue数据
quarterly_income = get_quarter_cumul_income(
report_date, cumul=False)[['ticker', 'quarter_revenue']]
# 读取市值与产业数据
ticker_property = get_ticker_property(
quarterly_income['ticker'].tolist(),
get_report_date(test_report_date, -1),
['industry', 'market_value'])
industry = ticker_property[['ticker', 'industry']]
market_value = ticker_property[['ticker', 'market_value']]
quarterly_income = pd.merge(quarterly_income, industry, on='ticker')
quarterly_income = pd.merge(quarterly_income,
market_value,
on='ticker')
quarterly_income['ratio_based_weight'] = quarterly_income[
'industry'].map(dict)
if ret.empty:
ret = quarterly_income
ret['ttm'] = ret['quarter_revenue'] * ret['ratio_based_weight']
ret.drop('ratio_based_weight', axis=1, inplace=True)
else:
ret = pd.merge(ret, quarterly_income, on='ticker')
ret['ttm'] += ret['quarter_revenue'] * ret['ratio_based_weight']
ret.drop('ratio_based_weight', axis=1, inplace=True)
ret.rename(
columns={'quarter_revenue': 'quarter_revenue_' + report_date},
inplace=True)
report_date_dt = datetime.strptime(test_report_date, '%Y-%m-%d')
quarter = report_date_dt.month // 4 + 1
ret['ttm'] = ret['ttm'] * quarter / 2.0
# 添加 行业和市值 信息
ticker_property = get_ticker_property(
ret['ticker'].tolist(), get_report_date(test_report_date, -1),
['industry', 'market_value'])
industry = ticker_property[['ticker', 'industry']]
market_value = ticker_property[['ticker', 'market_value']]
ret = pd.merge(ret, industry, on='ticker')
ret = pd.merge(ret, market_value, on='ticker')
# 去掉所有na
return ret
def ttm_model(test_report_date, alpha, year):
# 真实值
# 获得测试报告的真实累计营收
quarter_income_cumul = get_quarter_cumul_income(test_report_date)[[
'ticker', 'revenue'
]]
tickers = quarter_income_cumul['ticker'].tolist()
# 预测值
pred_quarter_income = revenue_predict(test_report_date, tickers, alpha,
year)
pred_quarter_income.drop(['market_value', 'industry'],
axis=1,
inplace=True)
ticker_property = get_ticker_property(tickers, test_report_date,
['industry', 'market_value'])
property = ticker_property[['ticker', 'industry', 'market_value']]
pred_quarter_income = pd.merge(pred_quarter_income, property, on='ticker')
# 合并到同一个DataFrame里面
ret = pd.merge(quarter_income_cumul, pred_quarter_income, on='ticker')
# 还是有个别ticker 是nan， 暂且去除掉
ret.dropna(inplace=True)
print('Baseline model weighted error: {0}'.format(
weighted_error(pred_y=ret['ttm'].values,
test_y=ret['revenue'].values,
market_values=ret['market_value'].values)))
return
def get_revenue_ttm(test_report_date):
"""
营业收入的ttm = 前四季度单季度营业收入的和
如果当前是2季度，则返回 ttm * 2 / 4
"""
# 获取当前报告日之前的四个季度报告日
prev_report_dates = [get_report_date(test_report_date, -i) for i in range(1, 5)]
ret = pd.DataFrame()
for report_date in prev_report_dates:
quarterly_income = get_quarter_cumul_income(report_date, cumul=False)[['ticker', 'quarter_revenue']]
if ret.empty:
ret = quarterly_income
ret['ttm'] = ret['quarter_revenue']
else:
ret = pd.merge(ret, quarterly_income, on='ticker')
ret['ttm'] += ret['quarter_revenue']
ret.rename(columns={'quarter_revenue': 'quarter_revenue_' + report_date}, inplace=True)
report_date_dt = datetime.strptime(test_report_date, '%Y-%m-%d')
quarter = report_date_dt.month // 4 + 1
ret['ttm'] = ret['ttm'] * quarter / 4.0
# 添加 行业和市值 信息
ticker_property = get_ticker_property(ret['ticker'].tolist(),
get_report_date(test_report_date, -1), ['industry', 'market_value'])
industry = ticker_property[['ticker', 'industry']]
market_value = ticker_property[['ticker', 'market_value']]
ret = pd.merge(ret, industry, on='ticker')
ret = pd.merge(ret, market_value, on='ticker')
# 去掉所有na
ret.dropna(inplace=True)
return ret
def revenue_predict(pred_report_date, tickers, alpha, year):
"""
预测本期的营业收入
如果要预测的ticker不在计算ttm的DataFrame中，那么以行业中市值相近的ticker替代
"""
# 获取营业收入的ttm值
# ret = get_ewma_revenue(pred_report_date, alpha, 4) # 此处有一超参数
# ret = get_revenue_ttm(pred_report_date)
ret = get_adjusted_ttm(pred_report_date, year)
ret = ret[ret['ticker'].isin(tickers)]
if len(tickers) > len(ret['ticker']):
missing_tickers = [ticker for ticker in tickers if ticker not in ret['ticker'].tolist()]
missing_tickers_property = get_ticker_property(missing_tickers,
get_report_date(pred_report_date, -1), ['industry', 'market_value'])
# unsolved_ticker = []
for ticker in missing_tickers_property['ticker'].tolist():
market_value = missing_tickers_property[missing_tickers_property['ticker'] == ticker]['market_value'].values[0]
industry = missing_tickers_property[missing_tickers_property['ticker'] == ticker]['industry'].values[0]
temp = ret[ret['industry'] == industry]
if temp.empty:
# unsolved_ticker.append(ticker)
continue
else:
closest_df = temp.iloc[(temp['market_value'] - market_value).abs().argsort()[:1]].copy()
closest_df.iloc[0, 0] = ticker
ret = pd.concat([ret, closest_df])
# ret.drop(unsolved_ticker, inplace=True)
return ret
def ttm_model(test_report_date, alpha, ttm_term, year, industry=None):
# 真实值
# 获得测试报告的真实累计营收
quarter_income_cumul = get_quarter_cumul_income(test_report_date)[[
'ticker', 'revenue'
]]
tickers = quarter_income_cumul['ticker'].tolist()
# 预测值
pred_quarter_income = revenue_predict(test_report_date, tickers, alpha,
ttm_term, year, industry)
pred_quarter_income.drop(['market_value', 'industry'],
axis=1,
inplace=True)
ticker_property = get_ticker_property(tickers, test_report_date,
['industry', 'market_value'])
property = ticker_property[['ticker', 'industry', 'market_value']]
pred_quarter_income = pd.merge(pred_quarter_income, property, on='ticker')
# 合并到同一个DataFrame里面
ret = pd.merge(quarter_income_cumul, pred_quarter_income, on='ticker')
ret.dropna(inplace=True)
# 追踪数据
ret.to_csv('../tracing_data_{0}.csv'.format(test_report_date))
w_err = weighted_error(pred_y=ret['ewma'].values,
test_y=ret['revenue'].values,
market_values=ret['market_value'].values)
print('Baseline model weighted error: {0}'.format(w_err))
return w_err
def get_revenue_ttm_ratio(test_report_date, year):
"""
营业收入的ttm ratio = 前四季度单季度营业收入增长率的均值
为方便计算，增长率取对数增长率，那么增长率的均值 = 1/4 *(log(x_4) - log(x_0))
修改：
增加year: 过去year年到过去一年数据的增长率平均值
"""
# 获取当前报告日之前的第一个季度报告日
prev_report_date = get_report_date(test_report_date, -4)
ret = get_quarter_cumul_income(prev_report_date,
cumul=False)[['ticker', 'quarter_revenue']]
# 获取当前报告日之前的第五个季度报告日
prev_report_date = get_report_date(test_report_date, -4 * year)
temp = get_quarter_cumul_income(prev_report_date,
cumul=False)[['ticker', 'quarter_revenue']]
ret = pd.merge(ret, temp, on='ticker')
ret.dropna(inplace=True)
ret['ttm_ratio'] = (np.log(ret['quarter_revenue_x']) -
np.log(ret['quarter_revenue_y'])) * (year - 1)
# 添加 行业和市值 信息
ticker_property = get_ticker_property(ret['ticker'].tolist(),
test_report_date,
['industry', 'market_value'])
industry = ticker_property[['ticker', 'industry']]
market_value = ticker_property[['ticker', 'market_value']]
ret = pd.merge(ret, industry, on='ticker')
ret = pd.merge(ret, market_value, on='ticker')
# 去掉所有na
ret.dropna(inplace=True)
return ret
def revenue_predict(pred_report_date, tickers, year):
"""
预测本期的营业收入
如果要预测的ticker不在计算ttm的DataFrame中，那么以行业中市值相近的ticker替代
"""
# 获取营业收入的ttm值
ret = get_revenue_ttm_ratio(pred_report_date, year)
ret = ret[ret['ticker'].isin(tickers)]
if len(tickers) > len(ret['ticker']):
missing_tickers = [
ticker for ticker in tickers
if ticker not in ret['ticker'].tolist()
]
missing_tickers_property = get_ticker_property(
missing_tickers, pred_report_date, ['industry', 'market_value'])
for ticker in missing_tickers_property['ticker'].tolist():
market_value = missing_tickers_property[
missing_tickers_property['ticker'] ==
ticker]['market_value'].values[0]
industry = missing_tickers_property[
missing_tickers_property['ticker'] ==
ticker]['industry'].values[0]
temp = ret[ret['industry'] == industry]
closest_df = temp.iloc[(temp['market_value'] -
market_value).abs().argsort()[:1]].copy()
closest_df.iloc[0, 0] = ticker
ret = pd.concat([ret, closest_df])
return ret
def cal_industry_ratio(test_report_date, year):
def get_quarter(date):
report_date_dt = datetime.strptime(date, '%Y-%m-%d')
quarter = report_date_dt.month // 4 + 1
return quarter
# industry_pool = get_quarter_cumul_income(test_report_date, cumul=False)['industry'].unique()
q1 = pd.DataFrame()
q2 = pd.DataFrame()
q3 = pd.DataFrame()
q4 = pd.DataFrame()
data_classified_by_quarter = [q1, q2, q3, q4]
for y in range(year):
sum_data = pd.DataFrame()
for i in range(1, 5):
back_q_num = y * 4 + i
prev_report_date = get_report_date(test_report_date, -back_q_num)
quarter = get_quarter(prev_report_date)
revenue_data = get_quarter_cumul_income(prev_report_date, cumul=False)[['ticker', 'quarter_revenue']]
# 生成总数
if i == 1:
sum_data = revenue_data[['ticker', 'quarter_revenue']]
else:
sum_data = pd.merge(sum_data, revenue_data[['ticker', 'quarter_revenue']], on='ticker')
sum_data['quarter_revenue'] = sum_data['quarter_revenue_x'] + sum_data['quarter_revenue_y']
sum_data.drop(['quarter_revenue_x', 'quarter_revenue_y'], axis=1, inplace=True)
sum_data.rename(columns={'quarter_revenue': 'sum'}, inplace=True)
# 计算占比
for i in range(1, 5):
back_q_num = y * 4 + i
prev_report_date = get_report_date(test_report_date, -back_q_num)
quarter = get_quarter(prev_report_date)
revenue_data = get_quarter_cumul_income(prev_report_date, cumul=False)[['ticker', 'quarter_revenue']]
revenue_data = pd.merge(revenue_data, sum_data, on='ticker')
revenue_data['ratio'] = revenue_data['quarter_revenue'] / revenue_data['sum']
# 合并industry
industry = get_ticker_property(revenue_data['ticker'].tolist(),
prev_report_date, ['industry'])
revenue_data = pd.merge(revenue_data, industry, on='ticker').drop(['quarter_revenue', 'sum'], axis=1)
if y == 0:
data_classified_by_quarter[quarter-1] = revenue_data
else:
data_classified_by_quarter[quarter-1] = pd.concat([data_classified_by_quarter[i-1], revenue_data], axis=0)
d1 = pd.DataFrame()
d2 = pd.DataFrame()
d3 = pd.DataFrame()
d4 = pd.DataFrame()
industry_ratio_map = [d1, d2, d3, d4]
for i in range(4):
industry_ratio_map[i] = data_classified_by_quarter[i].groupby('industry').mean()
return industry_ratio_map
def get_ewma_revenue(test_report_date, ttm_term=4, alpha=0.55, industry=None):
"""
:param test_report_date: 报告日期
:param alpha: 该系数越高，说明对过去值赋予的权重越低
:param ttm_term: 计算ewma的过去期数，虽然不是ttm，这样命名是为了模型系数统一
:return: ['ticker', 'ewma']
"""
prev_report_dates = [
get_report_date(test_report_date, -i) for i in range(1, ttm_term + 2)
]
ret = pd.DataFrame()
for report_date in prev_report_dates:
quarterly_income = get_quarter_cumul_income(
report_date, cumul=False)[['ticker', 'quarter_revenue']]
if ret.empty:
ret = quarterly_income
else:
ret = pd.merge(ret, quarterly_income, on='ticker')
ret.rename(columns={'quarter_revenue': report_date}, inplace=True)
# 以递归的方式计算ewma
def cal_ewma(test_date):
if test_date == prev_report_dates[-2]:
return alpha * ret[test_date] + (
1 - alpha) * ret[prev_report_dates[-1]]
else:
return alpha * ret[test_date] + (1 - alpha) * cal_ewma(
get_report_date(test_date, -1))
ewma_data = cal_ewma(get_report_date(test_report_date, -1))
ewma_data = pd.DataFrame(ewma_data, columns=['ewma'])
ewma_data = pd.concat([ret['ticker'], ewma_data], axis=1)
report_date_dt = datetime.strptime(test_report_date, '%Y-%m-%d')
quarter = report_date_dt.month // 4 + 1
ewma_data['ewma'] = ewma_data['ewma'] * quarter
# 添加 行业和市值 信息
ticker_property = get_ticker_property(
ret['ticker'].tolist(), get_report_date(test_report_date, -1),
['industry', 'market_value'])
industry_info = ticker_property[['ticker', 'industry']]
market_value = ticker_property[['ticker', 'market_value']]
ewma_data = pd.merge(ewma_data, industry_info, on='ticker')
ewma_data = pd.merge(ewma_data, market_value, on='ticker')
# 去掉所有na
ewma_data.dropna(inplace=True)
# """
if industry is None:
pass
else:
ewma_data = ewma_data[ewma_data['industry'].isin(list(industry))]
# """
return ewma_data
def get_adjusted_again_ttm(test_report_date, year, ttm_term=4):
ind_ratio_map = cal_ticker_ratio(test_report_date, year)
every_ticker_map = cal_ticker_ratio(test_report_date, year)
prev_report_dates = [
get_report_date(test_report_date, -i) for i in range(1, ttm_term + 1)
]
ret = pd.DataFrame() # 用于存放分季度的数据
for report_date in prev_report_dates:
quarter = get_quarter(report_date)
ind_map = ind_ratio_map[quarter - 1]
ticker_map = every_ticker_map[quarter - 1]
# 读取revenue数据
quarterly_income = get_quarter_cumul_income(
report_date, cumul=False)[['ticker', 'quarter_revenue']]
# quarterly_income.rename(columns={'quarter_revenue': quarter}, inplace=True)
# 读取市值与产业数据
# ticker_property = get_ticker_property(quarterly_income['ticker'].tolist(),
# get_report_date(test_report_date, -1), ['industry', 'market_value'])
# industry = ticker_property[['ticker', 'industry']]
# market_value = ticker_property[['ticker', 'market_value']]
# quarterly_income = pd.merge(quarterly_income, industry, on='ticker')
# quarterly_income = pd.merge(quarterly_income, market_value, on='ticker')
# 根据ticker_map中是否包含数据中的ticker将数据分成两组
in_data = quarterly_income[quarterly_income['ticker'].isin(
ticker_map.index)].copy() # 找得到对应ticker的数据
not_in_data = quarterly_income.drop(
in_data.index).copy() # 找不到dict中对应ticker的数据，用行业数据代替
# 处理in_data
in_data['ratio_based_weight'] = in_data['ticker'].map(
ticker_map['ratio'].to_dict())
# 处理not_in_data
if not_in_data.empty:
processed_data = in_data
else:
"""
ticker_property = get_ticker_property(not_in_data['ticker'].tolist(),
get_report_date(test_report_date, -1), ['industry', 'market_value'])
industry = ticker_property[['ticker', 'industry']]
not_in_data = pd.merge(not_in_data, industry, on='ticker')
not_in_data['ratio'] = not_in_data['ticker'].map(ind_map['ratio'].to_dict())
not_in_data.drop('industry', axis=1, inplace=True)
"""
not_in_data['ratio_based_weight'] = 0.25
# 纵向合并两个DF
processed_data = pd.concat([in_data, not_in_data])
if ret.empty:
ret = processed_data
ret['ttm'] = ret['quarter_revenue'] * ret['ratio_based_weight']
ret.drop('ratio_based_weight', axis=1, inplace=True)
else:
ret = pd.merge(ret, processed_data, on='ticker')
ret['ttm'] += ret['quarter_revenue'] * ret['ratio_based_weight']
ret.drop('ratio_based_weight', axis=1, inplace=True)
ret.rename(
columns={'quarter_revenue': 'quarter_revenue_' + report_date},
inplace=True)
report_date_dt = datetime.strptime(test_report_date, '%Y-%m-%d')
quarter = report_date_dt.month // 4 + 1
ret['ttm'] = ret['ttm'] * quarter / (ttm_term / 4.0)
# 添加 行业和市值 信息
ticker_property = get_ticker_property(
ret['ticker'].tolist(), get_report_date(test_report_date, -1),
['industry', 'market_value'])
industry = ticker_property[['ticker', 'industry']]
market_value = ticker_property[['ticker', 'market_value']]
ret = pd.merge(ret, industry, on='ticker')
ret = pd.merge(ret, market_value, on='ticker')
# 去掉所有na
return ret
def revenue_predict(pred_report_date,
tickers,
alpha=0.5,
ttm_term=4,
year=4,
industry=None):
"""
预测本期的营业收入
如果要预测的ticker不在计算ttm的DataFrame中，那么以行业中市值相近的ticker替代
method可选：'ttm', 'ewma', 'ind_ttm', 'ticker_ttm'
"""
# 获取营业收入的ttm值
ret = get_ewma_revenue(pred_report_date, ttm_term, alpha, industry)
ret = ret[ret['ticker'].isin(tickers)]
if len(tickers) > len(ret['ticker']):
missing_tickers = [
ticker for ticker in tickers
if ticker not in ret['ticker'].tolist()
]
missing_tickers_property = get_ticker_property(
missing_tickers, get_report_date(pred_report_date, -1),
['industry', 'market_value'])
if industry is None:
pass
else:
missing_tickers_property = missing_tickers_property[
missing_tickers_property['industry'].isin(industry)]
# unsolved_ticker = []
for ticker in missing_tickers_property['ticker'].tolist():
market_value = missing_tickers_property[
missing_tickers_property['ticker'] ==
ticker]['market_value'].values[0]
industry_info = missing_tickers_property[
missing_tickers_property['ticker'] ==
ticker]['industry'].values[0]
temp = ret[ret['industry'] == industry_info]
closest_df = temp.iloc[(temp['market_value'] -
market_value).abs().argsort()[:1]].copy()
closest_df.iloc[0, 0] = ticker
if temp.empty:
# unsolved_ticker.append(ticker)
continue
else:
closest_df = temp.iloc[(
temp['market_value'] -
market_value).abs().argsort()[:1]].copy()
closest_df.iloc[0, 0] = ticker
quarter = get_quarter(pred_report_date)
last_report_date = get_report_date(pred_report_date, -1)
last_quarter_revenue = get_quarter_cumul_income(
last_report_date, cumul=False)['ticker' == ticker]
if quarter == 1:
closest_df['ewma'] = last_quarter_revenue.iloc[0, 0]
else:
last_quarter_cumul_revenue = get_quarter_cumul_income(
last_report_date)['ticker' == ticker]
closest_df['ewma'] = last_quarter_revenue.iloc[
0, 0] + last_quarter_cumul_revenue.iloc[0, 0]
ret = pd.concat([ret, closest_df])
# ret.drop(unsolved_ticker, inplace=True)
return ret
