retreat commit

testy/alpacawebsokcet.py

@@ -0,0 +1,14 @@
+from alpaca.data.live.stock import StockDataStream
+from v2realbot.config import ACCOUNT2_PAPER_API_KEY, ACCOUNT2_PAPER_SECRET_KEY, ACCOUNT2_PAPER_FEED
+
+# keys required
+stock_stream = StockDataStream(ACCOUNT2_PAPER_API_KEY, ACCOUNT2_PAPER_SECRET_KEY, raw_data=True, websocket_params={}, feed=ACCOUNT2_PAPER_FEED)
+
+# async handler
+async def trade_data_handler(data):
+    # quote data will arrive here
+    print(data)
+
+stock_stream.subscribe_trades(trade_data_handler, "BAC")
+
+stock_stream.run()

testy/ml/numpybasics.py

@@ -0,0 +1,39 @@
+import numpy as np
+
+a = np.arange(20).reshape(2,2,5)
+print(a)
+
+#b = np.zeros((3,4))
+b = np.arange(12).reshape(3,4)
+print(b)
+#max z kazdeho sloupce
+c =  np.max(b, axis=0)
+#suma kazdeho radku
+c =  np.sum(b, axis=1)
+c = c.reshape(3,1)
+#sumu pridam na konec kazdeho radku, tzn.pripojim sloupce (horizontalne)
+d=np.hstack((b,c))
+print(d)
+
+#indexovani booleanem
+e = np.arange(12).reshape(3,4)
+f = e < 5
+print(e,f)
+print(e[f])
+#vsechny mensi nez 5 se stanou 0
+e[e<5] = 0
+print(e)
+
+
+# c = np.ones((2,2))
+# c = c.reshape(1,4)
+# print(c)
+# print(c*b)
+
+# d = np.arange(4).reshape(2,2)
+# e = d.copy()
+
+# print("d",d)
+# print("e",e)
+# print(d*e)
+# print(d@e)

testy/ml/numpyslicingtensors.py

@@ -0,0 +1,37 @@
+import numpy as np
+
+t = np.array([[[1,2,3,4,5], [2,2,2,2,2]],
+
+              [[3,3,3,3,3], [4,4,4,4,4]],
+
+              [[5,5,5,5,5], [6,6,6,6,6]]])
+
+print(t.shape, t.ndim, t.dtype)
+
+print(t[0:2].shape)
+
+
+#nasledujici je totozne
+a = t[:2]
+b = t[0:2, :, :]
+c = t[0:2, 0:2, 0:5]
+
+#posledni dve cisla kazdeho elementu tensoru 1
+a = t[1, :, 3:]
+
+#prostredni 3 cisla z kazdeho elementu
+a = t[:, :, 1:-1]
+# print(a==b)
+print(a)
+# print(b)
+# print(c)
+
+print(t.reshape((6,5)))
+print(t.reshape((30)))
+print(t.reshape((30,1)))
+print(np.transpose(t))
+
+
+#operations
+u =np.array([5,2,1,1,4])
+print(t+u)

testy/ml/numpytrendcolumn.py

@@ -0,0 +1,29 @@
+import numpy as np
+
+y = np.arange(20).reshape(4,5)
+print("y",y)
+# y = y[1:5:2,]
+y = y[::2,::2]
+print("y",y)
+
+
+# a = np.arange(30).reshape(-1,5)
+
+# #pole_posunute o radek dopredu - future a
+# fa = a.copy()
+# fa = fa[1:]
+# print("fa",fa)
+
+# #acko orizneme vzadu - aby byly stejne dlouhe
+# a = a[:-1]
+# print(a)
+# #a pak porovnáme jejich poslední sloupce a vysledek dáme jako další sloupec s 1 nebo 0
+# #nicmene melo by to nejak jit i bez pomocného pole
+# posl_sloupec=a[:,-1:]<fa[:,-1:]
+# print(posl_sloupec)
+# #sloupec 1/0 zda je hodnota nizsi nez hodnota o jeden radek vpredu
+# #tak si muzu nadefinovat 1ky kdyz je rising for 5 bars - udealt funkcni
+# a = np.hstack((a, posl_sloupec))
+# print(a)
+# #print(a[posl_sloupec>4])
+

testy/ml/test3_LSTMwindow.py

@@ -75,8 +75,8 @@ if res == 0:
 else:
     print("error",res,sada)
 
-bars = sada["bars"]
-indicators = sada["indicators"][0]
+# bars = sada["bars"]
+# indicators = sada["indicators"][0]
 
 # Zakladni nastaveni
 testlist_id = ""
@@ -86,11 +86,11 @@ indicator_features = ['samebarslope', 'fastslope','fsdelta', 'fastslope2', 'fsde
 features = ["time","high","low","volume","open","close", "trades", "vwap","samebarslope", "fastslope","fsdelta", "fastslope2", "fsdelta2"]
 #TODO toto je linearni prediction mod, dodelat podporu BINARY
 #u binary bude target bud hotovy indikator a nebo jej vytvorit on the fly
-target = 'vwap'
+target = 'close'
 #predict how many bars in the future
 target_steps = 5
 name = "model1"
-seq = 10
+seq = 2
 epochs = 500
 
 features.sort()