-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathF_Score选股小市值-Clone.py
248 lines (197 loc) · 9.66 KB
/
F_Score选股小市值-Clone.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
# 克隆自聚宽文章：https://www.joinquant.com/post/22160
# 标题：F_score计算函数及简单策略应用分享
# 作者：cgzol
# 导入函数库
from jqdata import *
# 初始化函数，设定基准等等
def initialize(context):
# 设定沪深300作为基准
set_benchmark('000300.XSHG')
# 开启动态复权模式(真实价格)
set_option('use_real_price', True)
# 输出内容到日志 log.info()
log.info('初始函数开始运行且全局只运行一次')
# 过滤掉order系列API产生的比error级别低的log
# log.set_level('order', 'error')
g.stock_index = '000300.XSHG'
### 股票相关设定 ###
# 股票类每笔交易时的手续费是：买入时佣金万分之三，卖出时佣金万分之三加千分之一印花税, 每笔交易佣金最低扣5块钱
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
## 运行函数（reference_security为运行时间的参考标的；传入的标的只做种类区分，因此传入'000300.XSHG'或'510300.XSHG'是一样的）
# 开盘前运行
run_weekly(before_market_open, time='before_open', reference_security='000300.XSHG',weekday=1)
# 开盘时运行
run_weekly(market_open, time='open', reference_security='000300.XSHG',weekday=1)
# 收盘后运行
run_weekly(after_market_close, time='after_close', reference_security='000300.XSHG',weekday=1)
## 开盘前运行函数
def before_market_open(context):
# 输出运行时间
log.info('函数运行时间(before_market_open)：'+str(context.current_dt.time()))
# 给微信发送消息（添加模拟交易，并绑定微信生效）
# send_message('美好的一天~')
# 要操作的股票：平安银行（g.为全局变量）
g.security = '000001.XSHE'
def check_risk(context):
today = context.current_dt
All = get_index_stocks(g.stock_index,today)
flowData = get_money_flow(All, end_date=today, fields=['sec_code','date','net_amount_xl','net_amount_l','net_amount_m','net_amount_s'], count=30)
preData = flowData.groupby(["sec_code"]).sum()
pre2Data = preData.loc[(preData['net_amount_xl'] > 0) & (preData['net_amount_l'] > 0)]
pre3Data = preData.loc[(preData['net_amount_xl'] > 0) & (preData['net_amount_l'] > 0) & (preData['net_amount_s'] > 0) ]
# pre4Data = preData.loc[(preData['net_amount_xl'] > 0) & (preData['net_amount_l'] > 0) ]
pre5Data = preData.loc[(preData['net_amount_s'] > 0) & (preData['net_amount_m'] > 0) ]
# pre6Data = preData.loc[(preData['net_amount_l'] > 0) & (preData['net_amount_s'] > 0) & (preData['net_amount_m'] > 0) ]
# print('pre5Data is len:%s'%len(pre5Data))
# print('pre6Data is len:%s'%len(pre6Data))
g.is_danger = False
if len(pre3Data) > 0:
# # if (len(pre3Data) <= 0 or (((len(pre3Data) <=10) and (len(pre4Data) <=10)) and ((len(pre5Data) <=10) and (len(pre6Data) <=15)) ) ):
# if ( len(pre5Data)/len(pre3Data) >250 ):
# return True
# else:
return False
else:
return True
def check_stock(context):
security_list = get_index_stocks(g.stock_index )
my_watch_date = context.current_dt
one_year_ago = my_watch_date - datetime.timedelta(days=365)
h = get_history_fundamentals(security_list,
[indicator.adjusted_profit,
balance.total_current_assets,
balance.total_assets,
balance.total_current_liability,
balance.total_non_current_liability,
cash_flow.net_operate_cash_flow,
income.operating_revenue,
income.operating_cost,
],
watch_date=my_watch_date, count=5).dropna() # 连续的5个季度
def ttm_sum(x):
return x.iloc[1:].sum()
def ttm_avg(x):
return x.iloc[1:].mean()
def pre_ttm_sum(x):
return x.iloc[:-1].sum()
def pre_ttm_avg(x):
return x.iloc[:-1].mean()
def val_1(x):
return x.iloc[-1]
def val_2(x):
if len(x.index) > 1:
return x.iloc[-2]
else:
return nan
def get_cap(x):
q = query(
valuation.market_cap
).filter(
valuation.code == x
)
return get_fundamentals(q,my_watch_date)['market_cap'].item()
# 扣非利润
adjusted_profit_ttm = h.groupby('code')['adjusted_profit'].apply(ttm_sum)
adjusted_profit_ttm_pre = h.groupby('code')['adjusted_profit'].apply(pre_ttm_sum)
# 总资产平均
total_assets_avg = h.groupby('code')['total_assets'].apply(ttm_avg)
total_assets_avg_pre = h.groupby('code')['total_assets'].apply(pre_ttm_avg)
# 经营活动产生的现金流量净额
net_operate_cash_flow_ttm = h.groupby('code')['net_operate_cash_flow'].apply(ttm_sum)
# 长期负债率: 长期负债/总资产
long_term_debt_ratio = h.groupby('code')['total_non_current_liability'].apply(val_1) / h.groupby('code')['total_assets'].apply(val_1)
long_term_debt_ratio_pre = h.groupby('code')['total_non_current_liability'].apply(val_2) / h.groupby('code')['total_assets'].apply(val_2)
# 流动比率：流动资产/流动负债
current_ratio = h.groupby('code')['total_current_assets'].apply(val_1) / h.groupby('code')['total_current_liability'].apply(val_1)
current_ratio_pre = h.groupby('code')['total_current_assets'].apply(val_2) / h.groupby('code')['total_current_liability'].apply(val_2)
# 营业收入
operating_revenue_ttm = h.groupby('code')['operating_revenue'].apply(ttm_sum)
operating_revenue_ttm_pre = h.groupby('code')['operating_revenue'].apply(pre_ttm_sum)
# 营业成本
operating_cost_ttm = h.groupby('code')['operating_cost'].apply(ttm_sum)
operating_cost_ttm_pre = h.groupby('code')['operating_cost'].apply(pre_ttm_sum)
# 1. ROA 资产收益率
roa = adjusted_profit_ttm / total_assets_avg
roa_pre = adjusted_profit_ttm_pre / total_assets_avg_pre
# 2. OCFOA 经营活动产生的现金流量净额/总资产
ocfoa = net_operate_cash_flow_ttm / total_assets_avg
# 3. ROA_CHG 资产收益率变化
roa_chg = roa - roa_pre
# 4. OCFOA_ROA 应计收益率: 经营活动产生的现金流量净额/总资产 -资产收益率
ocfoa_roa = ocfoa - roa
# 5. LTDR_CHG 长期负债率变化 (长期负债率=长期负债/总资产)
ltdr_chg = long_term_debt_ratio - long_term_debt_ratio_pre
# 6. CR_CHG 流动比率变化 (流动比率=流动资产/流动负债)
cr_chg = current_ratio - current_ratio_pre
# 8. GPM_CHG 毛利率变化 (毛利率=1-营业成本/营业收入)
gpm_chg = operating_cost_ttm_pre/operating_revenue_ttm_pre - operating_cost_ttm/operating_revenue_ttm
# 9. TAT_CHG 资产周转率变化(资产周转率=营业收入/总资产)
tat_chg = operating_revenue_ttm/total_assets_avg - operating_revenue_ttm_pre/total_assets_avg_pre
spo_list = list(set(finance.run_query(
query(
finance.STK_CAPITAL_CHANGE.code
).filter(
finance.STK_CAPITAL_CHANGE.code.in_(security_list),
finance.STK_CAPITAL_CHANGE.pub_date.between(one_year_ago, my_watch_date),
finance.STK_CAPITAL_CHANGE.change_reason_id == 306004)
)['code']))
spo_score = pd.Series(True, index = security_list)
if spo_list:
spo_score[spo_list] = False
df_scores = pd.DataFrame(index=security_list)
df_scores['vloum'] = df_scores.index.map(get_cap)
# 1
df_scores['roa'] = roa>0
# 2
df_scores['ocfoa'] = ocfoa>0
# 3
df_scores['roa_chg'] = ocfoa>0
# 4
df_scores['ocfoa_roa'] = ocfoa_roa>0
# 5
df_scores['ltdr_chg'] = ltdr_chg<=0
# 6
df_scores['cr_chg'] = cr_chg>0
# 7
df_scores['spo'] = spo_score
# 8
df_scores['gpm_chg'] = gpm_chg>0
# 9
df_scores['tat_chg'] = tat_chg>0
# 合计
df_scores = df_scores.dropna()
df_scores['total'] = df_scores['roa'] + df_scores['ocfoa'] + df_scores['roa_chg'] + \
df_scores['ocfoa_roa'] + df_scores['ltdr_chg'] + df_scores['cr_chg'] + \
df_scores['spo'] + df_scores['gpm_chg'] + df_scores['tat_chg']
res = df_scores.loc[lambda df_scores: df_scores['total'] > 8].sort_values(by ='vloum',ascending=True).head(5).index
return res
## 开盘时运行函数
def market_open(context):
log.info('函数运行时间(market_open):'+str(context.current_dt.time()))
security = g.security
hold_stock = context.portfolio.positions.keys()
if check_risk(context):
for s in hold_stock:
order_target(s,0)
else:
pool = check_stock(context)
# 获取股票的收盘价
if len(pool) >0:
cash = context.portfolio.total_value/len(pool)
#获取已经持仓列表
#卖出不在持仓中的股票
for s in hold_stock:
if s not in pool:
order_target(s,0)
#买入股票
for s in pool:
order_target_value(s,cash)
## 收盘后运行函数
def after_market_close(context):
log.info(str('函数运行时间(after_market_close):'+str(context.current_dt.time())))
#得到当天所有成交记录
trades = get_trades()
for _trade in trades.values():
log.info('成交记录：'+str(_trade))
log.info('一天结束')
log.info('##############################################################')