def makeBuyAction(api: sj.Shioaji, buy: list, choose: list):
for id in buy:
# if id in choose:
# con(api).StockNormalBuySell(stkid = id, price = "up", qty = 1, action = "Buy")
# logger.info(f"Buy {id}(漲停)")
# continue
con(api).StockNormalBuySell(stkid=id,
price="down",
qty=1,
action="Buy")
logger.info(f"Buy {id}(跌停)")
# 休息5秒,取得成交回報
time.sleep(5)
def writeDailyMinsKbarDataToDB(api=None):
no_update = []
tb = cfg().getValueByConfigFile(key="tb_mins")
sql = f"SELECT StockID, MAX(TradeDate) as TradeDate FROM {tb} group by StockID"
stkldayDF = db().selectDatatoDF(sql_statment=sql)
bcDF = db().selectDatatoDF(cfg().getValueByConfigFile(key="tb_basic"))
bcDF = bcDF.merge(stkldayDF, on=["StockID"], how="left")
for index, row in bcDF.iterrows():
try:
udate = row.TradeDate + timedelta(days=1)
except:
udate = date.today() - timedelta(days=300)
if udate > date.today():
continue
stkDF = con(api).getKbarData(
stkid=row.StockID,
sdate=udate.strftime("%Y-%m-%d"),
edate=date.today().strftime("%Y-%m-%d")).filter(items=[
"StockID", "TradeDate", "TradeTime", "Open", "High", "Low",
"Close", "Volume"
])
if stkDF.empty:
# 不是遇到週末才需要show沒有成功的部份
if udate.weekday() not in (5, 6):
no_update.append(row.StockID)
continue
stkDF = stkDF.drop_duplicates(
subset=["StockID", "TradeDate", "TradeTime"], keep="first")
print(f"StockID: {row.StockID}")
db().updateDFtoDB(stkDF, tb_name=tb)
if no_update != []:
print(f"沒有更新的Stock如下:{no_update}")
def getNewBuyDFforGetDealOrder(api_in, buylist: list):
outBuyDF = pd.DataFrame()
outbuylist = []
if GdealDF.empty:
con(api_in).StockCancelOrder()
sys.exit()
for idx, row in GdealDF.iterrows():
if row.Action == "Buy":
l = []
outbuylist.append(row.StockID)
l.append(row.StockID)
l.append(row.Price)
l.append(round(int(row.Price) * 1.01, 1))
l.append(round(int(row.Price) * (1 - 0.02), 1))
outBuyDF = outBuyDF.append(
[l], columns=["StockID", "Buy", "UP", "DOWN"])
diff = list(set(buylist).difference(outbuylist))
if diff != []:
for stkID in diff:
con(api_in).StockCancelOrder(stkID)
return outBuyDF
def writeDailyRawDataDB(api=None, StkDF: pd.DataFrame = None):
tb = cfg().getValueByConfigFile(key="tb_daily")
sql = f"SELECT StockID, MAX(TradeDate) as TradeDate FROM {tb} group by StockID"
lastday_stocks = db().selectDatatoDF(sql_statment=sql)
kBarDF = pd.DataFrame()
for index, row in StkDF.iterrows():
udate = datetime.strptime(row["update_date"], "%Y/%m/%d").date()
try:
lastday = lastday_stocks.loc[lastday_stocks.StockID ==
row["StockID"], "TradeDate"].values[0]
except:
lastday = udate - timedelta(days=400)
if lastday < udate:
DF = con(api).getKbarData(
stkid=row["StockID"],
sdate=(lastday + timedelta(days=1)).strftime("%Y-%m-%d"),
edate=udate.strftime("%Y-%m-%d"))
kBarDF = kBarDF.append(DF)
# 資料庫有資料 kBarDF就可能是空的
if not kBarDF.empty:
# kBarDF = kBarDF.filter(items = ["StockID", "TradeDate", "TradeTime", "Open", "High", "Low", "Close", "Volume"]).drop_duplicates(subset = ["StockID", "TradeDate", "TradeTime"], keep = "first")
kBarDF = kBarDF.filter(items=[
"StockID", "TradeDate", "TradeTime", "Open", "High", "Low",
"Close", "Volume"
])
DkBarDF = kBarDF.groupby(["StockID", "TradeDate"], sort=True).agg({
"Open":
"first",
"High":
max,
"Low":
min,
"Close":
"last",
"Volume":
sum
}).reset_index()
# 每日的OHLC資料
if not DkBarDF.empty:
db().updateDFtoDB(DkBarDF, tb_name=tb)
except Exception as exc:
logger.error(f"Tick to DF error! {exc}")
# return pd.DataFrame(getTrend, columns = ["StockID", "Trend"])
# 先檢查資料夾是否存在..沒有就建立
tool.checkCreateYearMonthPath()
pid = os.getpid()
# 開始log
logger = create_logger("./logs")
logger.info(f"Start PID = {pid}")
check_secs = 20
# 1.連接Server,指定帳號(預設chris),使用的CA(預設None)
api = con().ServerConnectLogin(ca="chris")
# api = con().ServerConnectLogin(simulte = True)
# 註:更換另一個帳號
# con(api).ChangeTradeCA(ca = "lydia")
# 1.1 設定回報Tick/Event資料
@api.on_tick_stk_v1()
def quote_callback(exchange: Exchange, tick: TickSTKv1):
global ticks
l = []
l.append(tick.code)
l.append(tick.datetime.strftime("%H:%M:%S.%f"))
l.append(tick.open)
# l.append(tick.avg_price)
# con(api).StockNormalBuySell(stkid = id, price = "down", qty = 1, action = "Buy")
# logger.info(f"Buy {id}(跌停)")
# # 休息5秒,取得成交回報
# time.sleep(5)
if __name__ == "__main__":
# 先檢查資料夾是否存在..沒有就建立
tool.checkCreateYearMonthPath()
# 開始log
logger = create_logger("./logs")
# 設定更新秒數
wait_secs = 20
# 1.連接Server,指定帳號(預設chris),使用的CA(預設None)
api = con().ServerConnectLogin(ca="chris")
# 2.取得股票清單(只留下可以當沖的)
stkDF = file.getPreviousTransactionFocusStockDF()
stkLst = tool.DFcolumnToList(stkDF, "StockID")
contracts = con(api).getContractForAPI(stkDF)
# 3.訂閱/回報
# 3.1 設定交易即時回報
api.set_order_callback(placeOrderCallBack)
# 3.2 設定回報資料(Tick / Bidask / Event)
@api.on_tick_stk_v1()
def quote_callback_tick(exchange: Exchange, tick: TickSTKv1):
global ticks
l = []
l.append(tick.code)
columns={
"ts": "TradeDateTime",
"close": "Close",
"volume": "Volume",
"bid_price": "BidPrice",
"bid_volume": "BidVolume",
"ask_price": "AskPrice",
"ask_volume": "AskVolume"
})
db().updateDFtoDB(tickDF, tb_name="dailyticks")
# 先檢查資料夾是否存在..沒有就建立
tool.checkCreateYearMonthPath()
api = con().ServerConnectLogin(user="******")
BsData = con(api).getStockDataByCondition()
writeDailyFocusStockTicks(api)
writeDailyMinsKbarDataToDB(api)
writeDailyKbarDataToDB(BsData)
writeDailyRawDataDB(api, BsData) # 這支去補足前面漏的
writeLegalPersonDailyVolumeDB(BsData)
# 取得每天的成交資料(後面數字是往回抓幾天)
stkDF = getStockDailyDataFromDB(BsData, 250)
stkDFwithInd = ind(
stkDF).addMAvalueToDF() # Default ma_type = SAR, period = [5, 10, 20, 60]
stkDFwithInd = ind(
stkDFwithInd).addBBANDvalueToDF() # Default period = 10, sigma = 2
def getExcuteTime():
extime = "09:05"
if sim().checkSimulationTime():
extime = (datetime.now() + timedelta(minutes = 2)).strftime("%H:%M")
return extime
# 先檢查資料夾是否存在..沒有就建立
tool.checkCreateYearMonthPath()
ticks = []
ticksDF = pd.DataFrame()
chk_sec = 20
# 1.連接Server,指定帳號(預設chris),使用的CA(預設None)
api = con().ServerConnectLogin( user = "******")
@api.on_tick_stk_v1()
def quote_callback(exchange: Exchange, tick:TickSTKv1):
global ticks
l = []
l.append(tick.code)
l.append(tick.datetime.strftime("%H:%M:%S.%f"))
l.append(tick.open)
# l.append(tick.avg_price)
l.append(tick.close)
l.append(tick.high)
l.append(tick.low)
# l.append(tick.amount)
# l.append(tick.total_amount)
l.append(tick.volume)