robo for hk

asset_pool/asset_optimize.py

@@ -77,10 +77,15 @@ class FundDividendSortinoAssetOptimize(SortinoAssetOptimize):
         self._navs = navs
         self._datum = datum
         self._conf = get_config(__name__)
+
     @property
     def asset_include(self):
         return self._conf['asset-include']
 
+    @property
+    def asset_filter(self):
+        return self._conf.get('asset-filter')
+
     @property
     def optimize_count(self):
         return self._conf['optimize-count']
@@ -127,11 +132,23 @@ class FundDividendSortinoAssetOptimize(SortinoAssetOptimize):
             last_one = rop.get_last_one(day=day, type=AssetPoolType.OPTIMIZE)
         return json.loads(last_one['asset_ids'])
 
+    def get_filtered_funds(self):
+        funds = self._datum.get_datums(type=DatumType.FUND)
+        if self.asset_filter:
+            filters = list(self.asset_filter.keys())[0]
+            funds_in = []
+            for fund in funds:
+                if fund[filters] in self.asset_filter[filters]:
+                    funds_in.append(fund)
+            return funds_in
+        return funds
+
     def get_groups(self):
-        funds = pd.DataFrame(self._datum.get_datums(type=DatumType.FUND))
+        funds = pd.DataFrame(self.get_filtered_funds())
         result = []
-        for (category, asset_type), fund_group in funds.groupby(by=['category', 'assetType']):
-            if category in self.asset_include:
+        include = list(self.asset_include.keys())[0]
+        for key, fund_group in funds.groupby(by=include):
+            if key in self.asset_include[include]:
                 result.append(tuple(fund_group['id']))
         return result
 

basic/datum.py

@@ -8,7 +8,7 @@ from api import DatumType, Datum, PortfoliosRisk, RoboExecutor
 from basic.dao import robo_base_datum as rbd
 
 
-@component
+@component(bean_name='datum')
 class DefaultDatum(Datum):
 
     def __init__(self):
@@ -79,3 +79,16 @@ class DefaultDatum(Datum):
                     }))
             return True
         return False
+
+
+@component(bean_name='datum')
+class HkDatum(DefaultDatum):
+    def get_datums(self, type: DatumType = None, crncy=None, risk=None, datum_ids=None, ticker=None, exclude=True):
+        datas = super().get_datums(type, crncy, None, datum_ids, ticker, exclude)
+        # 香港基金risk与现有risk有差异，全查出来再筛选
+        datas = [d for d in datas if d.get('hk')]
+        # 进行值的覆盖操作
+        datas = [d.update(d['hk']) or d for d in datas]
+        # 进行筛选操作
+        datas = [d for d in datas if not risk or d['risk'] == risk]
+        return datas

config-svrobo4.yml

+py-jftech:
+  logger:
+    version: 1
+    formatters:
+      brief:
+        format: "%(asctime)s - %(levelname)s - %(message)s"
+      simple:
+        format: "%(asctime)s - %(filename)s - %(levelname)s - %(message)s"
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        level: DEBUG
+        stream: ext://sys.stdout
+      file:
+        class: logging.handlers.TimedRotatingFileHandler
+        level: INFO
+        formatter: brief
+        filename: ${LOG_FILE:logs/info.log}
+        interval: 1
+        backupCount: 30
+        encoding: utf8
+        when: D
+#    loggers:
+#      basic.sync:
+#        level: DEBUG
+#        handlers: [console]
+#        propagate: no
+    root:
+      level: ${LOG_LEVEL:INFO}
+      handlers: ${LOG_HANDLERS:[ console ]}
+  database:
+    host: ${MYSQL_HOST:192.168.68.85}
+    port: ${MYSQL_PORT:3306}
+    user: ${MYSQL_USER:root}
+    password: ${MYSQL_PWD:changeit}
+    dbname: ${MYSQL_DBNAME:jftech_robo}
+  injectable:
+    names:
+      backtest: robo_executor.BacktestExecutor
+      datum: basic.datum.HkDatum
+      hold-report: portfolios.holder.DivHoldReportor
+      mpt: portfolios.builder.PoemPortfoliosBuilder
+#  email:
+#    server: smtphz.qiye.163.com
+#    user: jft-ra@thizgroup.com
+#    password: 5dbb#30ec6d3
+  mulit-process:
+    max-workers: ${MAX_PROCESS:4}
+basic: # 基础信息模块
+  sync:
+    start-date: 2007-01-01 # 同步数据开始日期
+  datum: # 资料模块
+    change:
+      date: ${DATUM_CHANGE_DATE}
+      file: ${DATUM_CHANGE_FILE}
+    excludes: # 排除的资料彭博ticker
+      backtest: []
+      real: []
+#  navs: # 净值模块
+#    exrate: # 汇率，如果不开启，整个这块注释掉
+#      - from: EUR # 需要转换的货币类型
+#        ticker: EURUSD BGN Curncy # 汇率值的彭博ticker
+asset-pool: # 资产池模块
+  asset-optimize: # 资产优选模块
+    sortino-weight: # sortino计算需要的权重，下面每一条为一次计算，e.g. months: 3, weight: 0.5 表示 3个月数据使用权重0.5来计算分值
+      - months: 3
+        weight: 0.5
+      - months: 6
+        weight: 0.3
+      - years: 1
+        weight: 0.2
+    asset-include: {'riskLevel':['LOW','HIGH']} #基金按照字典key分类，并筛选出value中的值，与下面normal-ratio设置的值相对应
+    asset-filter: {'risk':[1,2,3,4,5]} #基金按照字典key分类，并筛选出value中的值,仅作为筛选
+    optimize-count: 3 #基金优选个数
+portfolios: # 投组模块
+  holder: # 持仓投组相关
+    init-nav: 100000 # 初始金额
+    min-interval-days: 10 # 两次实际调仓最小间隔期，单位交易日
+    dividend-rate: 0 #设定年化配息率
+    dividend-drift-rate: 0.1  #超过基准配息率上下10%触发配息率重置
+    dividend-date: 15 #配息日，每月15号
+    dividend-adjust-day: []  #每年的首个季度调整配息
+    warehouse-frequency: 3  #每隔3个月调一次仓
+  solver: # 解算器相关
+    tol: 1E-10 # 误差满足条件
+    navs: # 净值要求
+      range: # 需要净值数据的区间, days: 90 表示90自然日，months: 3 表示3个自然月
+        days: 90
+      max-nan: # 最大缺失净值条件
+        asset: 8 # 单一资产最多缺少多少交易日数据，则踢出资产池
+        day: 0.5 # 单一交易日最多缺少百分之多少净值，则删除该交易日
+    normal-ratio: #分别对应低中高风险所占比率
+      LOW: [ 0.5, 0.5, 0.7 ]
+      HIGH: [ 0.5, 0.4, 0.2 ]
+    riskctl-ratio:
+      LOW: [ 0.5, 0.5, 0.7 ]
+      HIGH: [ 0.5, 0.4, 0.2 ]
+    matrix-rtn-days: 20 # 计算回报率矩阵时，回报率滚动天数
+    asset-count: [3,3] # 投组资产个数。e.g. count 或 [min, max] 分别表示 最大最小都为count 或 最小为min 最大为max，另外这里也可以类似上面给不同风险等级分别配置
+    mpt: # mpt计算相关
+      cvar-beta: 0.2 # 计算Kbeta 需要用到
+      quantile: 0.9 # 分位点，也可以给不同风险等级分别配置
+      low-weight: 0.05 # 最低权重
+#      high-weight: [ 1 ] # 最高权重比例，可给一个值，也可以给多个值，当多个值时，第一个表示只有一个资产时权重，第二个表示只有两个资产时权重，以此类推，最后一个表示其他资产个数时的权重
+    poem: # poem相关
+      cvar-scale-factor: 0.1 # 计算时用到的系数
+reports: # 报告模块相关
+  navs:
+    type: FUND
+    tickers:
+      - TEMTECI LX Equity
+      - TEPLX US Equity
+      - FRDPX US Equity
+      - FKRCX US Equity
+      - FTNRACU LX Equity
+  benchmark: # benchmark报告
+    ft:
+      init-amount: 100 # 初始金额
+      stock-rate: # stock型基金比例
+        RR3: 0.3
+        RR4: 0.5
+        RR5: 0.7
+  fixed-range: # 固定区间收益率
+    range-dates: # 固定起始截止日期
+      - 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: # 相对区间收益率
+    range-dates: # 相对时间周期
+      - days: 1
+        name: '一天'
+      - weeks: 1
+        name: '一周'
+      - months: 1
+        name: '一月'
+      - months: 3
+        name: '三月'
+      - months: 6
+        name: '六月'
+      - 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'
+      - dates: ~
+        name: '成立以来'
+  exports:
+    backtest: # 回测导出曹策略
+      save-path: ${EXPORT_PATH:excels} # 导出报告文件存放路径，如果以./或者../开头，则会以执行python文件为根目录，如果以/开头，则为系统绝对路径，否则，以项目目录为根目录
+      file-name: ${EXPORT_FILENAME:real} # 导出报告的文件名
+      save-config: ${EXPORT_CONFIG:off} # 是否保存配置文件
+      include-report: # 需要导出的报告类型列表，下面的顺序，也代表了excel中sheet的顺序
+  #      - funds-report # 基金资料
+  #      - navs-report # 净值报告
+        - hold-report # 持仓报告
+        - signal-report # 信号报告
+        - benckmark-report # benckmark报告
+        - combo-report # 持仓对比
+        - indicators-report # 各种特殊指标报告
+        - fixed-range-report # 固定区间收益报告
+        - relative-range-report # 相对区间收益报告
+        - year-range-report # 单年区间业绩报告
+        - month-div-rate-report # 月度配息率比较
+        - year-div-rate-report # 年度配息率比较
+    real-daily:
+      file-name: svROBO5_portfolios
+      include-report:
+        - daily-hold-report
+        - daily-signal-report
+      email:
+        receives:
+          - wenwen.tang@thizgroup.com
+        copies: ${DAILY_EMAIL_COPIES}
+        subject:
+          default: "ROBO5_TAIBEI-实盘版-每日投組推薦_{today}"
+          rebalance: "ROBO5_TAIBEI-实盘版-每日投組推薦_{today}_今日有調倉信號！！！"
+        content:
+          default: "Dear All: 附件是今天生成的推薦組合，請驗收，謝謝！ 注>：該郵件為自動發送，如有問題請聯繫矽谷團隊 telan_qian@chifufund.com"
+          rebalance: "Dear All: 附件是今天生成的推薦組合以及調倉信號，請驗收，謝謝！ 注>：該郵件為自動發送，如有問題請聯繫矽谷團隊 telan_qian@chifufund.com"
+    daily-monitor:
+      file-name: svROBO5_monitor
+      include-report:
+        - name: relative-range-report # 相对区间收益报告
+          min-date: ~
+        - name: contribution-report # 贡献率报告
+          min-date: {days: 30}
+        - name: high-weight-report # 高风险资产占比
+          min-date: {days: 30}
+        - name: asset-pool-report # 基金池
+          min-date: {days: 30}
+        - name: combo-report # 持仓报告
+          min-date: {days: 40}
+        - name: mpt-report
+          min-date: {days: 30}
+        - name: signal-report
+          min-date: ~
+        - name: crisis-one-report
+          min-date: {days: 30}
+        - name: crisis-two-report
+          min-date: {days: 30}
+        - name: market-right-report
+          min-date: {days: 30}
+        - name: drift-buy-report
+          min-date: {days: 30}
+      email:
+        receives:
+          - wenwen.tang@thizgroup.com
+        copies: ${MONITOR_EMAIL_COPIES}
+        subject: "SVROBO5-实盘版-每日监测_{today}"
+        content: "Dear All: 附件是今天生成的监测数据，請驗收，謝謝！ 注>：該郵件為自動發送，如有問題請聯繫矽谷團隊 telan_qian@chifufund.com"
+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:off} # 是否开启同步资料数据
+  backtest: # 回测执行器相关
+    start-date: 2012-10-16 # 回测起始日期
+    end-date: 2023-03-01 # 回测截止日期
+    sealing-period: 10 #调仓封闭期
+    start-step: ${BACKTEST_START_STEP:1} # 回测从哪一步开始执行 1:计算资产池；2:计算最优投组：3:计算再平衡信号以及持仓投组
+    end-step: ${BACKTEST_END_STEP:3} # 回测从哪一步执行完成后结束执行 1:计算资产池；2:计算最优投组：3:计算再平衡信号以及持仓投组
+    clean-up: true
+  real: # 实盘执行器
+    start-date: 2022-09-01 # 实盘开始时间
+
+
+
+

config-svrobo5.yml

@@ -30,14 +30,15 @@ py-jftech:
       level: ${LOG_LEVEL:INFO}
       handlers: ${LOG_HANDLERS:[ console ]}
   database:
-    host: ${MYSQL_HOST:::1}
+    host: ${MYSQL_HOST:192.168.68.85}
     port: ${MYSQL_PORT:3306}
     user: ${MYSQL_USER:root}
     password: ${MYSQL_PWD:changeit}
     dbname: ${MYSQL_DBNAME:jftech_robo}
   injectable:
-    name:
+    names:
       backtest: robo_executor.BacktestExecutor
+      datum: basic.datum.DefaultDatum
       hold-report: portfolios.holder.DivHoldReportor
       mpt: portfolios.builder.PoemPortfoliosBuilder
 #  email:
@@ -71,7 +72,7 @@ asset-pool: # 资产池模块
         weight: 0.3
       - years: 1
         weight: 0.2
-    asset-include: ['US_STOCK','US_IG_BOND','US_HY_BOND']
+    asset-include: {'category':['US_STOCK','US_IG_BOND','US_HY_BOND']}
     optimize-count: 3 #基金优选个数
 portfolios: # 投组模块
   holder: # 持仓投组相关
@@ -81,6 +82,7 @@ portfolios: # 投组模块
     dividend-drift-rate: 0.1  #超过基准配息率上下10%触发配息率重置
     dividend-date: 15 #配息日，每月15号
     dividend-adjust-day: [1,4,7,10]  #每年的首个季度调整配息
+    warehouse-frequency: 1  #每隔1个月调一次仓
   solver: # 解算器相关
     tol: 1E-10 # 误差满足条件
     navs: # 净值要求

portfolios/holder.py

@@ -8,7 +8,8 @@ from py_jftech import (
     component, autowired, get_config, next_workday, format_date, is_workday
 )
 
-from api import PortfoliosHolder, PortfoliosRisk, Navs, RoboExecutor, PortfoliosType, PortfoliosBuilder, RoboReportor
+from api import PortfoliosHolder, PortfoliosRisk, Navs, RoboExecutor, PortfoliosType, PortfoliosBuilder, RoboReportor, \
+    DatumType, Datum
 from portfolios.dao import robo_hold_portfolios as rhp
 from portfolios.utils import format_weight
 
@@ -19,12 +20,14 @@ logger = logging.getLogger(__name__)
 class DividendPortfoliosHolder(PortfoliosHolder):
 
     @autowired(names={'executor': RoboExecutor.use_name()})
-    def __init__(self, navs: Navs = None, executor: RoboExecutor = None, builder: PortfoliosBuilder = None):
+    def __init__(self, navs: Navs = None, executor: RoboExecutor = None, builder: PortfoliosBuilder = None,
+                 datum: Datum = None):
         self._navs = navs
         self._executor = executor
         self._builder = builder
         self._config = get_config(__name__)
         self._last_div = None
+        self._datum = datum
 
     def get_portfolio_type(self, day, risk: PortfoliosRisk) -> PortfoliosType:
         return PortfoliosType.NORMAL
@@ -78,9 +81,10 @@ class DividendPortfoliosHolder(PortfoliosHolder):
             #     每年的首个季度调整配息
             if day.month in self._config.get('dividend-adjust-day'):
                 asset_nav = last_nav['asset_nav']
+                # 配息率
                 div_rate = self._last_div * 12 / asset_nav
-                # 超过基准配息率上下10%触发配息率重置
-                if abs((self._config['dividend-rate'] - div_rate) / self._config['dividend-rate']) > \
+                # 年配息率减去配息率差值超过基准配息率上下10%触发配息率重置
+                if self.month_dividend > 0 and abs((self._config['dividend-rate'] - div_rate) / self._config['dividend-rate']) > \
                         self._config['dividend-drift-rate']:
                     # 以本月前一天的单位净值进行配息计算
                     dividend = last_nav['asset_nav'] * self.month_dividend
@@ -99,6 +103,7 @@ class DividendPortfoliosHolder(PortfoliosHolder):
                     dividend_acc = self._last_div + dividend_acc
             asset_nav = fund_av + fund_dividend + dividend
             nav = last_nav['nav'] * asset_nav / last_nav['asset_nav']
+            share = {x: fund_av * w / navs[x] for x, w in weight.items()}
         else:
             fund_av = self.init_nav
             fund_div_tuple = self.get_navs_and_div(fund_ids=tuple(weight), day=day)
@@ -109,7 +114,12 @@ class DividendPortfoliosHolder(PortfoliosHolder):
             dividend_acc = dividend + dividend_acc
             nav = self.init_nav
             asset_nav = fund_av + fund_dividend + dividend
-        share = {x: fund_av * w / navs[x] for x, w in weight.items()}
+            funds = self._datum.get_datums(type=DatumType.FUND)
+            funds_subscription_rate = {fund['id']: fund.get('subscriptionRate', 0) for fund in funds}
+            share = {x: (1-funds_subscription_rate[x])*(fund_av * w) / navs[x] for x, w in weight.items()}
+            #  初始买入扣手续费
+            fee = sum(funds_subscription_rate[x]*(fund_av * w) for x, w in weight.items())
+            fund_av = fund_av - fee
         rhp.insert({
             'date': day,
             'risk': risk,

portfolios/solver.py

@@ -202,7 +202,7 @@ class DefaultSolver(Solver):
         return sum(port_r_hist[0: self.k_beta]) / self.k_beta
 
     def get_weight(self):
-        # todo 根据self.risk找配置
+        # 根据asset-include中的对应key找配置
         return self.transfer_type[self.category][0]
 
     def create_model(self):
@@ -227,7 +227,8 @@ class DefaultSolver(Solver):
     def reset_navs(self, day):
         asset_ids = self._assets.get_pool(day)
         datum = self._datum.get_datums(type=DatumType.FUND, datum_ids=asset_ids)
-        asset_ids_group = {k: [d['id'] for d in datum if d['category'] == k] for k in set(d['category'] for d in datum)}
+        category = list(get_config('asset-pool')['asset-optimize']['asset-include'].keys())[0]
+        asset_ids_group = {k: [d['id'] for d in datum if d[category] == k] for k in set(d[category] for d in datum)}
         navs_group = {}
         for category, asset_ids in asset_ids_group.items():
             min_date = day - relativedelta(**self.get_config('navs.range'))

robo_executor.py

@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 @component(bean_name='backtest')
 class BacktestExecutor(RoboExecutor):
 
-    @autowired(names={'optimize': 'dividend'})
+    @autowired(names={'optimize': 'dividend', 'datum': 'hk-datum'})
     def __init__(self, datum: Datum = None, pool: AssetPool = None,
                  syncs: List[DataSync] = None, export: RoboExportor = None,
                  builder: PortfoliosBuilder = None, hold: PortfoliosHolder = None):
@@ -37,21 +37,22 @@ class BacktestExecutor(RoboExecutor):
     def get_first_business_day(start_date, end_date):
         # 生成日期范围并转换为DataFrame
         dates = pd.date_range(start_date, end_date, freq='MS')
+        dates = dates.insert(0, start_date)
         df = pd.DataFrame({'dates': dates})
         # 提取每个月的第一个工作日
         df['first_business_day'] = df['dates'].apply(
             lambda x: pd.date_range(start=x, end=x + pd.offsets.MonthEnd(0), freq='B')[0]
         )
-        # 返回第一个工作日列表
-        result = list(df['first_business_day'])
-        delta = workday_range(start_date, result[0])
+        # 每隔n个月提取第一个工作日
+        result = []
+        for i in range(0, len(df), get_config('portfolios')['holder']['warehouse-frequency']):
+            result.append(df.iloc[i]['first_business_day'])
+        delta = workday_range(result[0], result[1])
         period = get_config(__name__)['backtest']['sealing-period']
         if len(delta) <= period:
-            result.pop(0)
-        result.insert(0, start_date)
+            result.pop(1)
         return result
 
-
     @property
     def start_date(self):
         return pd.to_datetime(filter_weekend(self._config['start-date']))