导出模块完毕

.gitignore

@@ -4,4 +4,5 @@
 *.zip
 *.log
 /venv/
-/logs
+/logs
\ No newline at end of file
+/excels
\ No newline at end of file

api.py

 from abc import ABC, abstractmethod
 from datetime import datetime as dt
 from enum import Enum, unique
-from typing import List
+from typing import List, Dict
 
 from py_jftech import get_config
 
@@ -435,16 +435,24 @@ class PortfoliosHolder(ABC):
         pass
 
     @abstractmethod
-    def get_last_rebalance_date(self, risk: PortfoliosRisk, max_date=None, signal_id=None):
+    def get_last_rebalance_date(self, risk: PortfoliosRisk, max_date=None):
         '''
         获取最后一次实际调仓的时间
         :param risk: 持仓风险等级类型，必须
         :param max_date: 指定日期之前的最后一次，可选
-        :param signal_id: 指定信号的最后一次调仓，可选
         :return: 最后一次实际调仓的日期
         '''
         pass
 
+    @abstractmethod
+    def get_rebalance_date_by_signal(self, signal_id):
+        '''
+        获取指定调仓信号触发的实际调仓日期
+        :param signal_id: 指定的调仓信号
+        :return: 实际调仓日期
+        '''
+        pass
+
     @property
     @abstractmethod
     def interval_days(self):
@@ -523,10 +531,10 @@ class RebalanceRuler(ABC):
         pass
 
     @abstractmethod
-    def get_signal_type(self, sign_id) -> SignalType:
+    def get_signal_type(self, sign_id) -> SignalType | Dict[int, SignalType]:
         '''
         获取指定id的信号类型
-        :param sign_id: 信号id
+        :param sign_id: 信号id, 可以多个，使用元祖包裹
         :return: 信号类型
         '''
         pass
@@ -566,8 +574,11 @@ class RoboExecutor(ABC):
         return get_config('robo-executor')['use']
 
 
-class RoboReport(ABC):
-
+class RoboReportor(ABC):
+    '''
+    投组报告器
+    '''
+    @property
     @abstractmethod
     def report_name(self) -> str:
         '''
@@ -577,10 +588,27 @@ class RoboReport(ABC):
         pass
 
     @abstractmethod
-    def load_report(self, day=dt.today()) -> List[dict]:
+    def load_report(self, max_date=dt.today(), min_date=None) -> List[dict]:
         '''
         获取指定日期的报告
-        :param day: 指定日期
+        :param max_date: 指定截止日期
+        :param min_date: 指定开始日期
         :return: 报告数据
         '''
         pass
+
+
+class RoboExportor(ABC):
+    '''
+    投组导出器
+    '''
+
+    @abstractmethod
+    def export(self, max_date=dt.today(), min_date=None):
+        '''
+        导出指定日期的数据到excel
+        :param max_date: 指定截止日期
+        :param min_date: 指定开始日期
+        :return: 导出文件路径
+        '''
+        pass

config.yml

@@ -155,6 +155,57 @@ rebalance: # 再平衡模块
       cvar-min-volume: 30 # 计算cvar至少需要多少交易日数据
     high-low-buy: # 高低买入相关
       threshold: [ 0.5, 0.8 ] # [ 低买入阀值，高买入阀值 ]
+reports:
+  benchmark:
+    init-amount: 100
+    stock-rate:
+      RR3: 0.3
+      RR4: 0.5
+      RR5: 0.7
+  fixed-range:
+    range-dates:
+      - start: 2008-01-01
+        end: 2008-10-27
+      - start: 2011-05-02
+        end: 2011-10-04
+      - start: 2013-05-08
+        end: 2013-06-24
+      - start: 2014-09-03
+        end: 2014-12-16
+      - start: 2015-04-28
+        end: 2016-01-21
+      - start: 2018-01-26
+        end: 2018-10-29
+      - start: 2020-01-20
+        end: 2020-03-23
+  relative-range:
+    range-dates:
+      - months: 1
+        name: '一个月'
+      - months: 3
+        name: '三个月'
+      - months: 6
+        name: '六个月'
+      - years: 1
+        name: '一年'
+      - years: 2
+        name: '两年'
+      - years: 3
+        name: '三年'
+      - years: 5
+        name: '五年'
+      - years: 10
+        name: '十年'
+  backtest:
+    exist-build: on
+    save-path: ${EXPORT_PATH:excels}
+    include-report:
+      - hold-report
+      - signal-report
+      - benckmark-report
+      - indicators-report
+      - fixed-range-report
+      - relative-range-report
 robo-executor: # 执行器相关
   use: ${ROBO_EXECUTOR:backtest} #执行哪个执行器，优先取系统环境变量ROBO_EXECUTOR的值，默认backtest
   sync-data: ${SYNC_DATA:on}

portfolios/dao/robo_hold_portfolios.py

@@ -13,13 +13,26 @@ __COLUMNS__ = {
 }
 
 
+@read
+def get_list(risk: PortfoliosRisk = None, min_date=None, max_date=None, rebalance: bool = None):
+    sqls = []
+    if min_date:
+        sqls.append(f"rhp_date >= '{format_date(min_date)}'")
+    if max_date:
+        sqls.append(f"rhp_date <= '{format_date(max_date)}'")
+    return f'''
+    select {','.join([f'{x[0]} as {x[1]}' for x in __COLUMNS__.items()])} from robo_hold_portfolios
+    {where(*sqls, rhp_risk=risk, rhp_rebalance=rebalance)} order by rhp_risk, rhp_date
+    '''
+
+
 @read(one=True)
 def get_one(day, risk: PortfoliosRisk):
     return f'''select {','.join([f'{x[0]} as {x[1]}' for x in __COLUMNS__.items()])} from robo_hold_portfolios {where(rhp_date=day, rhp_risk=risk)}'''
 
 
 @read(one=True)
-def get_last_one(risk: PortfoliosRisk, max_date=None, rebalance: bool = None, signal_id=None):
+def get_last_one(risk: PortfoliosRisk = None, max_date=None, rebalance: bool = None, signal_id=None):
     sql = "rhp_date <= '{format_date(max_date)}'" if max_date else None
     return f'''
     select {','.join([f'{x[0]} as {x[1]}' for x in __COLUMNS__.items()])} from robo_hold_portfolios

portfolios/holder.py

 import json
 import logging
+from datetime import datetime as dt
+from typing import List
 
 import pandas as pd
 from py_jftech import (
     component, autowired, get_config, next_workday, prev_workday, transaction, workday_range, format_date
 )
 
-from api import PortfoliosHolder, PortfoliosRisk, RebalanceRuler, Navs, SignalType, RoboExecutor, PortfoliosType
+from api import PortfoliosHolder, PortfoliosRisk, RebalanceRuler, Navs, SignalType, RoboExecutor, PortfoliosType, RoboReportor
 from portfolios.dao import robo_hold_portfolios as rhp
 from portfolios.utils import format_weight
 
@@ -30,9 +32,13 @@ class NextReblanceHolder(PortfoliosHolder):
             return signal_type.p_type if signal_type else PortfoliosType.NORMAL
         return PortfoliosType.NORMAL
 
-    def get_last_rebalance_date(self, risk: PortfoliosRisk, max_date=None, signal_id=None):
+    def get_last_rebalance_date(self, risk: PortfoliosRisk, max_date=None):
         assert risk, f"get last rebalance date, risk can not be none"
-        last = rhp.get_last_one(max_date=max_date, risk=risk, signal_id=signal_id, rebalance=True)
+        last = rhp.get_last_one(max_date=max_date, risk=risk, rebalance=True)
+        return last['date'] if last else None
+
+    def get_rebalance_date_by_signal(self, signal_id):
+        last = rhp.get_last_one(signal_id=signal_id, rebalance=True)
         return last['date'] if last else None
 
     def get_portfolios_weight(self, day, risk: PortfoliosRisk):
@@ -127,3 +133,25 @@ class NextReblanceHolder(PortfoliosHolder):
     @property
     def init_nav(self):
         return self._config['init-nav']
+
+
+@component(bean_name='hold-report')
+class HoldReportor(RoboReportor):
+
+    @autowired
+    def __init__(self, rule: RebalanceRuler = None):
+        self._rule = rule
+
+    @property
+    def report_name(self) -> str:
+        return '投组净值'
+
+    def load_report(self, max_date=dt.today(), min_date=None) -> List[dict]:
+        holds = pd.DataFrame(rhp.get_list(max_date=max_date, min_date=min_date))
+        if not holds.empty:
+            signal_types = self._rule.get_signal_type(tuple(set(holds['signal_id'])))
+            holds['signal_type'] = holds.apply(lambda row: signal_types[row['signal_id']].name, axis=1)
+            holds['risk'] = holds.apply(lambda row: PortfoliosRisk(row['risk']).name, axis=1)
+            holds = holds[['risk', 'date', 'nav', 'signal_type']]
+            return holds.to_dict('records')
+        return []

portfolios/test_case.py

 import logging
 import unittest
 
-from py_jftech import autowired, parse_date
+from py_jftech import autowired, parse_date, to_str
 
-from api import PortfoliosBuilder, PortfoliosType, PortfoliosRisk, PortfoliosHolder, SolveType
-from portfolios.dao import robo_mpt_portfolios as rmp
-from pymysql import IntegrityError, constants
+from api import PortfoliosBuilder, PortfoliosType, PortfoliosRisk, PortfoliosHolder, RoboReportor
 
 
 class PortfoliosTest(unittest.TestCase):
@@ -34,6 +32,11 @@ class PortfoliosTest(unittest.TestCase):
         hold.build_hold_portfolio(parse_date('2016-01-01'), PortfoliosRisk.FT9)
         pass
 
+    @autowired(names={'reportor': 'hold-report'})
+    def test_hold_report(self, reportor: RoboReportor = None):
+        report = reportor.load_report()
+        self.logger.info(to_str(report))
+
 
 if __name__ == '__main__':
     unittest.main()

rebalance/dao/robo_rebalance_signal.py

-from py_jftech import read, write, where, format_date, mapper_columns
+from py_jftech import read, write, where, format_date, mapper_columns, to_tuple
 
 from api import SignalType, PortfoliosRisk
 
@@ -13,10 +13,27 @@ __COLUMNS__ = {
 }
 
 
+@read
+def get_list(min_date=None, max_date=None, risk: PortfoliosRisk = None, type: SignalType = None, effective: bool = None):
+    sqls = []
+    if min_date:
+        sqls.append(f"rrs_date >= '{format_date(min_date)}'")
+    if max_date:
+        sqls.append(f"rrs_date <= '{format_date(max_date)}'")
+    return f'''
+    select {','.join([f"{x[0]} as {x[1]}" for x in __COLUMNS__.items()])} from robo_rebalance_signal
+    {where(*sqls, rrs_risk=risk, rrs_type=type, rrs_effective=effective)} order by rrs_risk, rrs_date
+    '''
+
+
+@read
+def get_by_ids(ids):
+    return f'''select {','.join([f"{x[0]} as {x[1]}" for x in __COLUMNS__.items()])} from robo_rebalance_signal {where(rrs_id=to_tuple(ids))}'''
+
+
 @read(one=True)
 def get_by_id(id):
-    return f'''
-    select {','.join([f"{x[0]} as {x[1]}" for x in __COLUMNS__.items()])} from robo_rebalance_signal {where(rrs_id=id)}'''
+    return f'''select {','.join([f"{x[0]} as {x[1]}" for x in __COLUMNS__.items()])} from robo_rebalance_signal {where(rrs_id=id)}'''
 
 
 @read(one=True)
@@ -47,6 +64,7 @@ def get_count(risk: PortfoliosRisk = None, day=None, effective=None):
     @read(one=True)
     def exec():
         return f"select count(*) as `count` from robo_rebalance_signal {where(rrs_risk=risk, rrs_date=day, rrs_effective=effective)}"
+
     result = exec()
     return result['count']
 

rebalance/ruler.py

-from typing import List
+from datetime import datetime as dt
+from typing import List, Dict
+import json
 
-from py_jftech import component, autowired, get_config, workday_range, next_workday
+import pandas as pd
+import numpy as np
+from py_jftech import component, autowired, get_config, workday_range, next_workday, to_tuple
 
-from api import RebalanceRuler, PortfoliosRisk, RebalanceSignal, SignalType, PortfoliosType, PortfoliosHolder
+from api import RebalanceRuler, PortfoliosRisk, RebalanceSignal, SignalType, PortfoliosType, PortfoliosHolder, RoboReportor, Datum, DatumType
 from rebalance.dao import robo_rebalance_signal as rrs
 
 
@@ -64,12 +68,48 @@ class LevelRebalanceRuler(RebalanceRuler):
                 return signal
         return None
 
-    def get_signal_type(self, sign_id) -> SignalType:
-        signal = rrs.get_by_id(sign_id)
-        return SignalType(signal['type']) if signal else None
+    def get_signal_type(self, sign_id) -> SignalType | Dict[int, SignalType]:
+        sign_id = to_tuple(sign_id)
+        if len(sign_id) > 1:
+            return {x['id']: SignalType(x['type']) for x in rrs.get_by_ids(sign_id)}
+        else:
+            signal = rrs.get_by_id(sign_id[0])
+            return SignalType(signal['type']) if signal else None
 
     def commit_signal(self, sign_id):
         rrs.update(sign_id, {'effective': True})
 
     def clear_signal(self, day=None, risk: PortfoliosRisk = None):
         rrs.delete(min_date=day, risk=risk)
+
+
+@component(bean_name='signal-report')
+class SignalExportor(RoboReportor):
+
+    @autowired
+    def __init__(self, hold: PortfoliosHolder = None, datum: Datum = None):
+        self._hold = hold
+        self._datum = datum
+
+    @property
+    def report_name(self) -> str:
+        return '调仓信号'
+
+    def load_report(self, max_date=dt.today(), min_date=None) -> List[dict]:
+        result = []
+        datums = {str(x['id']): x for x in self._datum.get_datums(type=DatumType.FUND)}
+        for signal in rrs.get_list(max_date=max_date, min_date=min_date, effective=True):
+            rebalance_date = self._hold.get_rebalance_date_by_signal(signal['id'])
+            for fund_id, weight in json.loads(signal['portfolio']).items():
+                result.append({
+                    'risk': PortfoliosRisk(signal['risk']).name,
+                    'type': SignalType(signal['type']).name,
+                    'signal_date': signal['date'],
+                    'rebalance_date': rebalance_date,
+                    'portfolio_type': PortfoliosType(signal['portfolio_type']).name,
+                    'ft_ticker': datums[fund_id]['ftTicker'],
+                    'blooberg_ticker': datums[fund_id]['bloombergTicker'],
+                    'fund_name': datums[fund_id]['chineseName'],
+                    'weight': weight
+                })
+        return result

rebalance/test_case.py

 import logging
 import unittest
 
-from py_jftech import autowired, parse_date
+from py_jftech import autowired, parse_date, to_str
 
-from api import RebalanceSignal, PortfoliosRisk, RebalanceRuler
+from api import RebalanceSignal, PortfoliosRisk, RebalanceRuler, RoboReportor
 
 
 class RebalanceTest(unittest.TestCase):
@@ -33,6 +33,11 @@ class RebalanceTest(unittest.TestCase):
     def test_rebalance_builder(self, builder: RebalanceRuler = None):
         builder.take_next_signal(parse_date('2022-09-01'), PortfoliosRisk.FT3)
 
+    @autowired(names={'reportor': 'signal-report'})
+    def test_signal_report(self, reportor: RoboReportor = None):
+        result = reportor.load_report()
+        self.logger.info(to_str(result, show_line=10))
+
 
 if __name__ == '__main__':
     unittest.main()

reports/backtest.py

+import os
+from datetime import datetime as dt
+from typing import List
+
+import pandas as pd
+from py_jftech import component, autowired, get_config, get_instance_name, get_project_path, format_date
+
+from api import RoboReportor, RoboExportor
+
+
+def include_report():
+    return get_config(__name__)['include-report']
+
+
+@component(bean_name='backtest-export')
+class BacktestExportor(RoboExportor):
+
+    @autowired(includes={'reportors': include_report()})
+    def __init__(self, reportors: List[RoboReportor] = None):
+        reportors = {get_instance_name(x): x for x in reportors}
+        self._reportors: List[RoboReportor] = [reportors[x] for x in include_report()]
+        self._config = get_config(__name__)
+
+    @property
+    def save_path(self):
+        save_path: str = self._config['save-path']
+        if save_path.startswith('.'):
+            return os.path.abspath(os.path.join(os.path.dirname(__file__), save_path))
+        elif save_path.startswith('/'):
+            return os.path.abspath(save_path)
+        return os.path.abspath(os.path.join(get_project_path(), save_path))
+
+    @property
+    def exist_build(self):
+        return self._config['exist-build']
+
+    def export(self, max_date=dt.today(), min_date=None):
+        root = self.save_path
+        os.makedirs(root, exist_ok=True)
+        filename = f"backtest_{format_date(max_date)}.xlsx"
+        if min_date:
+            filename = f"backtest_{format_date(min_date)}_to_{format_date(max_date)}.xlsx"
+        file = os.path.join(root, filename)
+        if os.path.exists(file):
+            if not self.exist_build:
+                return file
+            os.remove(file)
+        with pd.ExcelWriter(file) as writer:
+            for reportor in self._reportors:
+                datas = pd.DataFrame(reportor.load_report(max_date=max_date, min_date=min_date))
+                if not datas.empty:
+                    datas.to_excel(writer, sheet_name=reportor.report_name, index=False)
+        return file
+
+

reports/benchmark.py

+from datetime import datetime as dt, timedelta
+from typing import List
+
+import pandas as pd
+from py_jftech import component, autowired, prev_workday, filter_weekend, next_workday, get_config
+
+from api import RoboReportor, PortfoliosRisk, RoboExecutor, Navs, Datum, DatumType
+from reports.dao import robo_benckmark as rb
+
+
+@component(bean_name='benckmark-report')
+class BenchmarkReportor(RoboReportor):
+
+    @autowired(names={'executor': RoboExecutor.use_name()})
+    def __init__(self, executor: RoboExecutor = None, navs: Navs = None, datum: Datum = None):
+        self._exec = executor
+        self._navs = navs
+        self._datum = datum
+        self._config = get_config(__name__)
+
+    @property
+    def report_name(self) -> str:
+        return 'benchmark'
+
+    @property
+    def risks(self):
+        return self._config['stock-rate'].keys()
+
+    @property
+    def init_amount(self):
+        return self._config['init-amount']
+
+    def stock_rate(self, risk):
+        return self._config['stock-rate'][risk]
+
+    def load_nav_rtn(self, risk, day):
+        last = rb.get_last_one(risk=risk, max_date=day, re=True)
+        start_date = last['date'] if last else next_workday(self._exec.start_date)
+
+        datums = {x['id']: x for x in self._datum.get_datums(type=DatumType.FUND)}
+        navs = pd.DataFrame(self._navs.get_fund_navs(fund_ids=tuple(datums.keys()), min_date=prev_workday(start_date - timedelta(10)), max_date=day))
+        navs = navs.pivot_table(index='nav_date', columns='fund_id', values='nav_cal')
+        navs.fillna(method='ffill', inplace=True)
+        nav_index = navs.shape[1]
+        for i in range(nav_index):
+            navs[f'rtn_{navs.columns[i]}'] = navs[navs.columns[i]] / navs[navs.columns[i]].shift() - 1
+        navs = navs[navs.index >= start_date]
+        return navs, nav_index
+
+    def find_datum_asset(self):
+        return {x['id']: x['assetType'] for x in self._datum.get_datums(type=DatumType.FUND)}
+
+    def build_benchmark(self, risk, day=dt.today()):
+        nav_rtn, nav_index = self.load_nav_rtn(risk=risk, day=day)
+        asset_types = {x['id']: x['assetType'] for x in self._datum.get_datums(type=DatumType.FUND)}
+
+        last = rb.get_last_one(risk=risk, max_date=day, re=True)
+        init_amount = last['nav'] if last else self.init_amount
+        stock_rate = self.stock_rate(risk)
+        other_rate = 1 - stock_rate
+
+        five_rtn = 0
+        last_day = None
+        fund_ids = None
+
+        for index, row in nav_rtn.iterrows():
+            if last_day is None or fund_ids is None:
+                fund_ids = list(row.iloc[:nav_index].dropna().index)
+                stock_count = len([x for x in fund_ids if asset_types[x] == 'STOCK'])
+                stock_average = init_amount * stock_rate / stock_count
+                other_average = init_amount * other_rate / (len(fund_ids) - stock_count)
+                nav_rtn.loc[index, f'{risk}_result'] = init_amount
+                nav_rtn.loc[index, f'{risk}_re'] = 1
+                for fund_id in fund_ids:
+                    if fund_id and asset_types[fund_id] == 'STOCK':
+                        nav_rtn.loc[index, f'stock_{fund_id}'] = stock_average
+                    else:
+                        nav_rtn.loc[index, f'other_{fund_id}'] = other_average
+            else:
+                for fund_id in fund_ids:
+                    if fund_id and asset_types[fund_id] == 'STOCK':
+                        nav_rtn.loc[index, f'stock_{fund_id}'] = nav_rtn.loc[last_day, f'stock_{fund_id}'] * (
+                                1 + nav_rtn.loc[index, f'rtn_{fund_id}'])
+                    else:
+                        nav_rtn.loc[index, f'other_{fund_id}'] = nav_rtn.loc[last_day, f'other_{fund_id}'] * (
+                                1 + nav_rtn.loc[index, f'rtn_{fund_id}'])
+                nav_rtn.loc[index, f'{risk}_result'] = nav_rtn.loc[index][-len(fund_ids):].sum()
+                nav_rtn.loc[index, f'{risk}_re'] = 0
+            if five_rtn == 5:
+                five_rtn = 0
+                fund_ids = list(row.iloc[:nav_index].dropna().index)
+                stock_count = len([x for x in fund_ids if asset_types[x] == 'STOCK'])
+
+                stock_average = nav_rtn.loc[index, f'{risk}_result'] * stock_rate / stock_count
+                other_average = nav_rtn.loc[index, f'{risk}_result'] * other_rate / (len(fund_ids) - stock_count)
+                nav_rtn.loc[index, f'{risk}_re'] = 1
+                for fund_id in fund_ids:
+                    if fund_id and asset_types[fund_id] == 'STOCK':
+                        nav_rtn.loc[index, f'stock_{fund_id}'] = stock_average
+                    else:
+                        nav_rtn.loc[index, f'other_{fund_id}'] = other_average
+            five_rtn += 1
+            last_day = index
+
+        result = nav_rtn.reindex(columns=[f'{risk}_result', f'{risk}_re'])
+        result.reset_index(inplace=True)
+        result['risk'] = risk
+        result.rename(columns={f'{risk}_result': 'nav', f'{risk}_re': 're', 'nav_date': 'date'}, inplace=True)
+        result['nav'] = round(result['nav'], 4)
+        return result.to_dict('records')
+
+    def load_report(self, max_date=dt.today(), min_date=None) -> List[dict]:
+        for risk in self.risks:
+            last = rb.get_last_one(max_date=max_date, risk=risk)
+            if not last or last['date'] < filter_weekend(max_date):
+                benchmarks = pd.DataFrame(self.build_benchmark(risk=risk, day=max_date))
+                if last:
+                    benchmarks = benchmarks[benchmarks.date > last['date']]
+                if not benchmarks.empty:
+                    rb.batch_insert(benchmarks.to_dict('records'))
+        result = pd.DataFrame(rb.get_list(max_date=max_date, min_date=min_date))
+        result = result.pivot_table(index='date', columns='risk', values='nav')
+        result.reset_index(inplace=True)
+        return result.to_dict('records')
+

reports/combo_datas.py

+from datetime import datetime as dt
+from typing import List
+
+import pandas as pd
+from py_jftech import component, autowired
+from empyrical import annual_return, annual_volatility, max_drawdown, sharpe_ratio
+
+from api import RoboReportor, Navs
+
+
+@component(bean_name='combo-report')
+class ComboDatasReport(RoboReportor):
+
+    @autowired(names={'hold_reportor': 'hold-report', 'benchmark': 'benckmark-report'})
+    def __init__(self, hold_reportor: RoboReportor = None, benchmark: RoboReportor = None, navs: Navs = None):
+        self._hold_reportor = hold_reportor
+        self._benchmark = benchmark
+        self._navs = navs
+
+    @property
+    def report_name(self) -> str:
+        return '混合数据'
+
+    def load_report(self, max_date=dt.today(), min_date=None) -> List[dict]:
+        holds = pd.DataFrame(self._hold_reportor.load_report(max_date=max_date, min_date=min_date))
+        if not holds.empty:
+            holds['risk'] = holds.apply(lambda row: row.risk, axis=1)
+            datas = holds.pivot_table(index='date', columns='risk', values='nav')
+
+            benchmark = pd.DataFrame(self._benchmark.load_report(max_date=max_date, min_date=min_date))
+            datas = datas.join(benchmark.set_index('date'))
+
+            spx = pd.DataFrame(self._navs.get_index_close(ticker='SPX Index', min_date=min_date, max_date=max_date))
+            spx = spx.pivot_table(index='date', columns='index_id', values='close')
+            spx.columns = ['SPX']
+            datas = datas.join(spx)
+
+            datas.fillna(method='ffill', inplace=True)
+            datas.reset_index(inplace=True)
+            return datas.to_dict('records')
+        return []
+

reports/dao/mysql.sql

+CREATE TABLE IF NOT EXISTS robo_benchmark
+(
+    rb_id          BIGINT UNSIGNED NOT NULL AUTO_INCREMENT,
+    rb_date        DATETIME        NOT NULL COMMENT '日期',
+    rb_risk        VARCHAR(255)    NOT NULL COMMENT '风险等级',
+    rb_nav         DOUBLE(16, 4)   NOT NULL COMMENT '资产值',
+    rb_re          TINYINT         NOT NULL DEFAULT 0 COMMENT '是否再分配',
+    rb_create_time DATETIME        NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    rb_update_time DATETIME                 DEFAULT NULL ON UPDATE CURRENT_TIMESTAMP,
+    PRIMARY KEY (rb_id),
+    UNIQUE INDEX (rb_date, rb_risk),
+    INDEX (rb_risk)
+) ENGINE = InnoDB
+  AUTO_INCREMENT = 0
+  DEFAULT CHARSET = utf8mb4 COMMENT 'BENCHMARK数据表';
\ No newline at end of file

reports/dao/robo_benckmark.py

+from py_jftech import read, write, where, mapper_columns, format_date
+
+__COLUMNS__ = {
+    'rb_id': 'id',
+    'rb_date': 'date',
+    'rb_risk': 'risk',
+    'rb_nav': 'nav',
+    'rb_re': 're',
+}
+
+
+@write
+def batch_insert(datas):
+    datas = [mapper_columns(x, __COLUMNS__) for x in datas]
+    values = ','.join([f'''({','.join([(f"'{x[j]}'" if j in x and x[j] is not None else 'null') for j in __COLUMNS__.keys() if j != 'rb_id'])})''' for x in datas])
+    return f'''insert into robo_benchmark({','.join([x for x in __COLUMNS__.keys() if x != 'rb_id'])}) values {values}'''
+
+
+@read(one=True)
+def get_last_one(max_date=None, risk=None, re: bool = None):
+    sql = f"rb_date <= '{format_date(max_date)}'" if max_date else None
+    return f'''
+    select {','.join([f"{x[0]} as {x[1]}" for x in __COLUMNS__.items()])} from robo_benchmark
+    {where(sql, rb_risk=risk, rb_re=re)} order by rb_date desc limit 1
+    '''
+
+
+@read
+def get_list(max_date=None, min_date=None, risk=None, re: bool = None):
+    sqls = []
+    if max_date:
+        sqls.append(f"rb_date <= '{format_date(max_date)}'")
+    if min_date:
+        sqls.append(f"rb_date >= '{format_date(min_date)}'")
+    return f'''
+    select {','.join([f"{x[0]} as {x[1]}" for x in __COLUMNS__.items()])} from robo_benchmark
+    {where(*sqls, rb_risk=risk, rb_re=re)} order by rb_risk, rb_date
+    '''

reports/fixed_range.py

+from datetime import datetime as dt
+from typing import List
+
+import pandas as pd
+from py_jftech import component, autowired, get_config, format_date
+
+from api import RoboReportor
+
+
+@component(bean_name='fixed-range-report')
+class FixedRangeReport(RoboReportor):
+
+    @autowired(names={'combo': 'combo-report'})
+    def __init__(self, combo: RoboReportor = None):
+        self._combo = combo
+        self._config = get_config(__name__)
+
+    @property
+    def report_name(self) -> str:
+        return '固定区间收益率'
+
+    @property
+    def range_dates(self):
+        return self._config['range-dates']
+
+    def load_report(self, max_date=dt.today(), min_date=None) -> List[dict]:
+        datas = pd.DataFrame(self._combo.load_report(max_date=max_date, min_date=min_date))
+        datas.set_index('date', inplace=True)
+        if not datas.empty:
+            result = pd.DataFrame(columns=datas.columns)
+            for range in self.range_dates:
+                row_name = f"{format_date(range['start'])}~{format_date(range['end'])}"
+                result.loc[row_name] = datas[range['start']:range['end']].values[-1] / datas[range['start']:range['end']].values[0] - 1
+            result = round(result, 4) * 100
+            result.reset_index(inplace=True)
+            result.rename(columns={'index': 'range-date'}, inplace=True)
+            return result.to_dict('records')
+        return []

reports/indicators.py

+from datetime import datetime as dt
+from typing import List
+
+import pandas as pd
+from empyrical import annual_return, annual_volatility, max_drawdown, sharpe_ratio
+from py_jftech import component, autowired
+
+from api import RoboReportor
+
+
+@component(bean_name='indicators-report')
+class IndicatorsReportor(RoboReportor):
+
+    @autowired(names={'combo': 'combo-report'})
+    def __init__(self, combo: RoboReportor = None):
+        self._combo = combo
+
+    @property
+    def report_name(self) -> str:
+        return '指标'
+
+    def load_report(self, max_date=dt.today(), min_date=None) -> List[dict]:
+        datas = pd.DataFrame(self._combo.load_report(max_date=max_date, min_date=min_date))
+        datas.set_index('date', inplace=True)
+        if not datas.empty:
+            returns = round(datas.pct_change(), 5)
+            indicators = {
+                'annual_return': list(annual_return(returns, period='daily', annualization=None) * 100),
+                'annual_volatility': annual_volatility(returns, period='daily', annualization=None) * 100,
+                'max_drawdown': max_drawdown(returns, out=None) * 100,
+                'sharp': sharpe_ratio(returns, risk_free=0, period='daily', annualization=None),
+            }
+            indicators['calmar'] = abs(indicators['annual_return'] / indicators['max_drawdown'])
+            result = pd.DataFrame(indicators.values(), index=indicators.keys(), columns=list(returns.columns)).round(2)
+            result.reset_index(inplace=True)
+            result.rename(columns={'index': 'indicators'}, inplace=True)
+            return result.to_dict('records')
+        return []

reports/relative_range.py

+from datetime import datetime as dt
+from typing import List
+
+import pandas as pd
+from dateutil.relativedelta import relativedelta
+from py_jftech import component, autowired, get_config, format_date
+
+from api import RoboReportor
+
+
+@component(bean_name='relative-range-report')
+class RelativeRangeReport(RoboReportor):
+
+    @autowired(names={'combo': 'combo-report'})
+    def __init__(self, combo: RoboReportor = None):
+        self._combo = combo
+        self._config = get_config(__name__)
+
+    @property
+    def report_name(self) -> str:
+        return '相对区间收益率'
+
+    @property
+    def range_dates(self):
+        return self._config['range-dates']
+
+    def load_report(self, max_date=dt.today(), min_date=None) -> List[dict]:
+        datas = pd.DataFrame(self._combo.load_report(max_date=max_date, min_date=min_date))
+        datas.set_index('date', inplace=True)
+        if not datas.empty:
+            result = pd.DataFrame(columns=datas.columns)
+            for range in self.range_dates:
+                kwargs = range.copy()
+                del kwargs['name']
+                start = max_date - relativedelta(**kwargs)
+                row_name = f"{range['name']}({format_date(start)}~{format_date(max_date)})"
+                result.loc[row_name] = datas[start:max_date].values[-1] / datas[start:max_date].values[0] - 1
+            result = round(result, 4) * 100
+            result.reset_index(inplace=True)
+            result.rename(columns={'index': 'range-date'}, inplace=True)
+            return result.to_dict('records')
+        return []

reports/test_case.py

+import unittest
+import logging
+
+from py_jftech import autowired, to_str, parse_date
+from api import RoboReportor, RoboExportor
+
+logger = logging.getLogger(__name__)
+
+
+class ReportTest(unittest.TestCase):
+
+    @autowired(names={'reportor': 'benckmark-report'})
+    def test_benchmark_report(self, reportor: RoboReportor = None):
+        result = reportor.load_report(max_date=parse_date('2022-11-01'))
+        logger.info(to_str(result))
+
+    @autowired(names={'reportor': 'indicators-report'})
+    def test_indicator_report(self, reportor: RoboReportor = None):
+        result = reportor.load_report(max_date=parse_date('2022-11-01'))
+        logger.info(to_str(result))
+
+    @autowired(names={'reportor': 'fixed-range-report'})
+    def test_fixed_report(self, reportor: RoboReportor = None):
+        result = reportor.load_report(max_date=parse_date('2022-11-01'))
+        logger.info(to_str(result))
+
+    @autowired(names={'reportor': 'relative-range-report'})
+    def test_relative_report(self, reportor: RoboReportor = None):
+        result = reportor.load_report(max_date=parse_date('2022-11-01'))
+        logger.info(to_str(result))
+
+    @autowired(names={'reportor': 'backtest-export'})
+    def test_backtest_export(self, exportor: RoboExportor = None):
+        path = exportor.export(max_date=parse_date('2022-11-01'))
+        logger.info(path)
+
+
+if __name__ == '__main__':
+    unittest.main()

requirements.txt

 certifi==2022.9.24
 charset-normalizer==2.1.1
 empyrical==0.5.5
+et-xmlfile==1.1.0
 idna==3.4
 lxml==4.9.0
 numpy==1.23.4
+openpyxl==3.0.10
 pandas==1.5.1
 pandas-datareader==0.10.0
 ply==3.11