# -*- coding: utf-8 -*-
"""
author: zengbin93
email: zeng_bin8888@163.com
create_dt: 2023/9/24 15:19
describe: 策略持仓权重管理
"""
import os
import time
import json
import redis
import threading
import pandas as pd
from loguru import logger
from datetime import datetime
[docs]class RedisWeightsClient:
"""策略持仓权重收发客户端"""
version = "V231112"
def __init__(self, strategy_name, redis_url=None, send_heartbeat=True, **kwargs):
"""
:param strategy_name: str, 策略名
:param redis_url: str, redis连接字符串, 默认为None, 即从环境变量 RWC_REDIS_URL 中读取
For example::
redis://[[username]:[password]]@localhost:6379/0
rediss://[[username]:[password]]@localhost:6379/0
unix://[username@]/path/to/socket.sock?db=0[&password=password]
Three URL schemes are supported:
- `redis://` creates a TCP socket connection. See more at:
<https://www.iana.org/assignments/uri-schemes/prov/redis>
- `rediss://` creates a SSL wrapped TCP socket connection. See more at:
<https://www.iana.org/assignments/uri-schemes/prov/rediss>
- ``unix://``: creates a Unix Domain Socket connection.
:param send_heartbeat: boolean, 是否发送心跳
如果为True，会在后台启动一个线程，每15秒向redis发送一次心跳，用于检测策略是否存活。
推荐在写入数据时设置为True，读取数据时设置为False，避免无用的心跳。
:param kwargs: dict, 其他参数
- key_prefix: str, redis中key的前缀，默认为 Weights
- heartbeat_prefix: str, 心跳key的前缀，默认为 heartbeat
"""
self.strategy_name = strategy_name
self.redis_url = redis_url if redis_url else os.getenv("RWC_REDIS_URL")
self.key_prefix = kwargs.get("key_prefix", "Weights")
thread_safe_pool = redis.BlockingConnectionPool.from_url(self.redis_url, decode_responses=True)
self.r = redis.Redis(connection_pool=thread_safe_pool)
self.lua_publish = RedisWeightsClient.register_lua_publish(self.r)
if send_heartbeat:
self.heartbeat_client = redis.from_url(self.redis_url, decode_responses=True)
self.heartbeat_prefix = kwargs.get("heartbeat_prefix", "heartbeat")
self.heartbeat_thread = threading.Thread(target=self.__heartbeat, daemon=True)
self.heartbeat_thread.start()
@property
def metadata(self):
"""获取策略元数据"""
key = f'{self.key_prefix}:META:{self.strategy_name}'
return self.r.hgetall(key)
[docs] def get_last_times(self, symbols=None):
"""获取所有品种上策略最近一次发布信号的时间
:param symbols: list, 品种列表, 默认为None, 即获取所有品种
:return: dict, {symbol: datetime}，如{'SFIF9001': datetime(2021, 9, 24, 15, 19, 0)}
"""
if isinstance(symbols, str):
row = self.r.hgetall(f'{self.key_prefix}:{self.strategy_name}:{symbols}:LAST')
return pd.to_datetime(row['dt']) if row else None # type: ignore
symbols = symbols if symbols else self.get_symbols()
with self.r.pipeline() as pipe:
for symbol in symbols:
pipe.hgetall(f'{self.key_prefix}:{self.strategy_name}:{symbol}:LAST')
rows = pipe.execute()
return {x['symbol']: pd.to_datetime(x['dt']) for x in rows}
[docs] def publish(self, symbol, dt, weight, price=0, ref=None, overwrite=False):
"""发布单个策略持仓权重
:param symbol: str, eg; SFIF9001
:param dt: py_datetime or pandas Timestamp
:param weight: float, 信号值
:param price: float, 产生信号时的价格
:param ref: dict, 自定义数据
:param overwrite: boolean, 是否覆盖已有记录
:return: 成功发布信号的条数
"""
if not isinstance(dt, datetime):
dt = pd.to_datetime(dt)
if not overwrite:
last_dt = self.get_last_times(symbol)
if last_dt is not None and dt <= last_dt: # type: ignore
logger.warning(f"不允许重复写入，已过滤 {symbol} {dt} 的重复信号")
return 0
udt = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
key = f'{self.key_prefix}:{self.strategy_name}:{symbol}:{dt.strftime("%Y%m%d%H%M%S")}'
ref = ref if ref else '{}'
ref_str = json.dumps(ref) if isinstance(ref, dict) else ref
return self.lua_publish(keys=[key], args=[1 if overwrite else 0, udt, weight, price, ref_str])
[docs] def publish_dataframe(self, df, overwrite=False, batch_size=10000):
"""批量发布多个策略信号
:param df: pandas.DataFrame, 必需包含['symbol', 'dt', 'weight']列,
可选['price', 'ref']列, 如没有price则写0, dtype同publish方法
:param overwrite: boolean, 是否覆盖已有记录
:return: 成功发布信号的条数
"""
df = df.copy()
df['dt'] = pd.to_datetime(df['dt'])
logger.info(f"输入数据中有 {len(df)} 条权重信号")
# 去除单个品种下相邻时间权重相同的数据
_res = []
for _, dfg in df.groupby('symbol'):
dfg = dfg.sort_values('dt', ascending=True).reset_index(drop=True)
dfg = dfg[dfg['weight'].diff().fillna(1) != 0].copy()
_res.append(dfg)
df = pd.concat(_res, ignore_index=True)
df = df.sort_values(['dt']).reset_index(drop=True)
logger.info(f"去除单个品种下相邻时间权重相同的数据后，剩余 {len(df)} 条权重信号")
if 'price' not in df.columns:
df['price'] = 0
if 'ref' not in df.columns:
df['ref'] = '{}'
if not overwrite:
raw_count = len(df)
_time = self.get_last_times()
_data = []
for symbol, dfg in df.groupby('symbol'):
last_dt = _time.get(symbol)
if last_dt is not None:
dfg = dfg[dfg['dt'] > last_dt]
_data.append(dfg)
df = pd.concat(_data, ignore_index=True)
logger.info(f"不允许重复写入，已过滤 {raw_count - len(df)} 条重复信号")
keys, args = [], []
for row in df[['symbol', 'dt', 'weight', 'price', 'ref']].to_numpy():
key = f'{self.key_prefix}:{self.strategy_name}:{row[0]}:{row[1].strftime("%Y%m%d%H%M%S")}'
keys.append(key)
args.append(row[2])
args.append(row[3])
ref = row[4]
args.append(json.dumps(ref) if isinstance(ref, dict) else ref)
udt = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
overwrite = 1 if overwrite else 0
pub_cnt = 0
len_keys = len(keys)
for i in range(0, len_keys, batch_size):
if i + batch_size < len_keys:
tmp_keys = keys[i: i + batch_size]
tmp_args = [overwrite, udt] + args[3 * i: 3 * (i + batch_size)]
else:
tmp_keys = keys[i: len_keys]
tmp_args = [overwrite, udt] + args[3 * i: 3 * len_keys]
logger.info(f"索引 {i}，即将发布 {len(tmp_keys)} 条权重信号")
pub_cnt += self.lua_publish(keys=tmp_keys, args=tmp_args)
logger.info(f"已完成 {pub_cnt} 次发布")
return pub_cnt
def __heartbeat(self):
while True:
key = f'{self.key_prefix}:{self.heartbeat_prefix}:{self.strategy_name}'
try:
self.heartbeat_client.set(key, datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
except Exception:
continue
time.sleep(15)
[docs] def get_keys(self, pattern):
"""获取 redis 中指定 pattern 的 keys"""
return self.r.keys(pattern)
[docs] def clear_all(self):
"""删除该策略所有记录"""
self.r.delete(f'{self.key_prefix}:META:{self.strategy_name}')
keys = self.get_keys(f'{self.key_prefix}:{self.strategy_name}*')
if keys is not None and len(keys) > 0: # type: ignore
self.r.delete(*keys) # type: ignore
[docs] @staticmethod
def register_lua_publish(client):
lua_body = '''
local overwrite = ARGV[1]
local update_time = ARGV[2]
local cnt = 0
local ret
for i = 1, #KEYS do
local key = KEYS[i]
local sig = ARGV[3 + 3 * (i - 1)]
local price = ARGV[4 + 3 * (i - 1)]
local ref_str = ARGV[5 + 3 * (i - 1)]
local split_str = {}
key:gsub('[^:]+', function(s) table.insert(split_str, s) end)
local model_key = split_str[1] .. ':' .. split_str[2] .. ':' .. split_str[3]
local if_pass = true
if overwrite ~= '0' then
if_pass = false
else
local pos = redis.call('HGET', model_key .. ':LAST', 'weight')
if not pos or math.abs(tonumber(sig) - tonumber(pos)) > 0.00001 then
if_pass = false
end
end
if not if_pass then
local strategy_name, symbol, action_time = split_str[2], split_str[3], split_str[4]
local at_str = string.sub(action_time, 1, 4) .. '-' .. string.sub(action_time, 5, 6) .. '-' ..
string.sub(action_time, 7, 8) .. ' ' .. string.sub(action_time, 9, 10) .. ':' ..
string.sub(action_time, 11, 12) .. ':' .. string.sub(action_time, 13, 14)
redis.call('ZADD', model_key, tonumber(action_time), key)
local ret1 = redis.call('HMSET', key, 'symbol', symbol, 'weight', sig, 'dt', at_str, 'update_time', update_time, 'price', price, 'ref', ref_str)
local ret2 = redis.call('HMSET', key:gsub(action_time, 'LAST'), 'symbol', symbol, 'weight', sig, 'dt', at_str, 'update_time', update_time, 'price', price, 'ref', ref_str)
if ret1.ok and ret2.ok then
cnt = cnt + 1
local pubKey = 'PUBSUB:' .. split_str[1] .. ':' .. strategy_name .. ':' .. symbol
redis.call('PUBLISH', pubKey, key .. ':' .. sig .. ':' .. price .. ':' .. ref_str)
end
end
end
return cnt
'''
return client.register_script(lua_body)
[docs] def get_symbols(self):
"""获取策略交易的品种列表"""
keys = self.get_keys(f'{self.key_prefix}:{self.strategy_name}*')
symbols = {x.split(":")[2] for x in keys} # type: ignore
return list(symbols)
[docs] def get_last_weights(self, symbols=None, ignore_zero=True, lua=True):
"""获取最近的持仓权重
:param symbols: list, 品种列表
:param ignore_zero: boolean, 是否忽略权重为0的品种
:param lua: boolean, 是否使用 lua 脚本获取，默认为True
如果要全量获取，推荐使用 lua 脚本，速度更快；如果要获取指定 symbols，不推荐使用 lua 脚本。
:return: pd.DataFrame
"""
if lua:
lua_script = """
local keys = redis.call('KEYS', ARGV[1])
local results = {}
for i=1, #keys do
results[i] = redis.call('HGETALL', keys[i])
end
return results
"""
key_pattern = self.key_prefix + ':' + self.strategy_name + ':*:LAST'
results = self.r.eval(lua_script, 0, key_pattern)
rows = [dict(zip(r[::2], r[1::2])) for r in results] # type: ignore
if symbols:
rows = [r for r in rows if r['symbol'] in symbols]
else:
symbols = symbols if symbols else self.get_symbols()
with self.r.pipeline() as pipe:
for symbol in symbols:
pipe.hgetall(f'{self.key_prefix}:{self.strategy_name}:{symbol}:LAST')
rows = pipe.execute()
dfw = pd.DataFrame(rows)
dfw['weight'] = dfw['weight'].astype(float)
dfw['dt'] = pd.to_datetime(dfw['dt'])
if ignore_zero:
dfw = dfw[dfw['weight'] != 0].copy().reset_index(drop=True)
dfw = dfw.sort_values(['dt', 'symbol']).reset_index(drop=True)
return dfw
[docs] def get_hist_weights(self, symbol, sdt, edt) -> pd.DataFrame:
"""获取单个品种的持仓权重历史数据
:param symbol: str, 品种代码
:param sdt: str, 开始时间, eg: 20210924 10:19:00
:param edt: str, 结束时间, eg: 20220924 10:19:00
:return: pd.DataFrame
"""
start_score = pd.to_datetime(sdt).strftime('%Y%m%d%H%M%S')
end_score = pd.to_datetime(edt).strftime('%Y%m%d%H%M%S')
model_key = f'{self.key_prefix}:{self.strategy_name}:{symbol}'
key_list = self.r.zrangebyscore(model_key, start_score, end_score)
if len(key_list) == 0:
logger.warning(f'no history weights: {symbol} - {sdt} - {edt}')
return pd.DataFrame()
with self.r.pipeline() as pipe:
for key in key_list:
pipe.hmget(key, 'weight', 'price', 'ref')
rows = pipe.execute()
weights = []
for i in range(len(key_list)):
dt = pd.to_datetime(key_list[i].split(":")[-1])
weight, price, ref = rows[i]
weight = weight if weight is None else float(weight)
price = price if price is None else float(price)
try:
ref = json.loads(ref)
except Exception:
ref = ref
weights.append((self.strategy_name, symbol, dt, weight, price, ref))
dfw = pd.DataFrame(weights, columns=['strategy_name', 'symbol', 'dt', 'weight', 'price', 'ref'])
dfw = dfw.sort_values('dt').reset_index(drop=True)
return dfw
[docs] def get_all_weights(self, sdt=None, edt=None, **kwargs) -> pd.DataFrame:
"""获取所有权重数据
:param sdt: str, 开始时间, eg: 20210924 10:19:00
:param edt: str, 结束时间, eg: 20220924 10:19:00
:return: pd.DataFrame
"""
lua_script = """
local keys = redis.call('KEYS', ARGV[1])
local results = {}
for i=1, #keys do
local last_part = keys[i]:match('([^:]+)$')
if #last_part == 14 and tonumber(last_part) ~= nil then
results[#results + 1] = redis.call('HGETALL', keys[i])
end
end
return results
"""
key_pattern = self.key_prefix + ':' + self.strategy_name + ':*:*'
results = self.r.eval(lua_script, 0, key_pattern)
results = [dict(zip(r[::2], r[1::2])) for r in results] # type: ignore
df = pd.DataFrame(results)
df['dt'] = pd.to_datetime(df['dt'])
df['weight'] = df['weight'].astype(float)
df = df.sort_values(['dt', 'symbol']).reset_index(drop=True)
df1 = pd.pivot_table(df, index='dt', columns='symbol', values='weight').sort_index().ffill().fillna(0)
df1 = pd.melt(df1.reset_index(), id_vars='dt', value_vars=df1.columns, value_name='weight') # type: ignore
if sdt:
df1 = df1[df1['dt'] >= pd.to_datetime(sdt)].reset_index(drop=True)
if edt:
df1 = df1[df1['dt'] <= pd.to_datetime(edt)].reset_index(drop=True)
df1 = df1.sort_values(['dt', 'symbol']).reset_index(drop=True)
return df1