fix issues

longterm_baseline.py

@@ -2,14 +2,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-import pickle
-
-# [(Snapshot_Time, [(buy_price1, amount1), ...] , [(sell_price1, amount1), ...]), ...]
-# [(1620457034392, [(56000, 0.01), (55900, 1), (55700, 30), ...] , [(57000, 0.01), (57100, 1), ...] ), (1620457034394, [...]), ...]
-# The snapshots should has almost identical time-interval. Good for LSTM. 
-# Time axis: [history, older, newer, ..., latest]
-realtime_shortterm_dataset_depth = []
-realtime_shortterm_dataset_depth_size = 1024
+import pickle, numpy, math
 
 # [(Trade_Time, PRICE, AMOUNT), ...]
 # [(1620457034392, 56000, 0.5), (1620457034394, 56001, 0.05), ...]
@@ -18,6 +11,13 @@ realtime_shortterm_dataset_depth_size = 1024
 realtime_shortterm_dataset_aggtrade = []
 realtime_shortterm_dataset_aggtrade_size = 1024*1024
 
+# [(Snapshot_Time, [(buy_price1, amount1), ...] , [(sell_price1, amount1), ...]), ...]
+# [(1620457034392, [(56000, 0.01), (55900, 1), (55700, 30), ...] , [(57000, 0.01), (57100, 1), ...] ), (1620457034394, [...]), ...]
+# The snapshots should has almost identical time-interval. Good for LSTM. 
+# Time axis: [history, older, newer, ..., latest]
+realtime_shortterm_dataset_depth = []
+realtime_shortterm_dataset_depth_size = 1024
+
 # The trading thread would not start working, before finish analysing longterm dataset. 
 # Time-interval for longterm dataset is 1 minute.
 longterm_dataset = []
@@ -35,7 +35,7 @@ def load_realtime_dataset_on_start():
 class LSTM_Shortterm_Predictor(nn.Module):
     def __init__(self, input_dim):
         super(LSTM_Shortterm_Predictor, self).__init__()
-        self.lstm_idim = 16
+        self.lstm_idim = 10 # this is the length of depth_to_impulsive_score_vector()
         self.lstm_odim = 128
 
         # The input would be a tuple containing complex information. 
@@ -51,7 +51,7 @@ class LSTM_Shortterm_Predictor(nn.Module):
         self.out = nn.Linear(self.lstm_odim, 1)
 
     def forward(self, sample_seq):
-        input_seq = sample_seq.view(len(sample_seq), 1, lstm_idim)
+        input_seq = sample_seq.view(len(sample_seq), 1, self.lstm_idim)
         lstm_in = self.serializer(input_seq)
         lstm_out, _ = self.lstm(lstm_in)
         predict_shortterm_trend = self.out(torch.tanh(lstm_out[-1:]))
@@ -82,18 +82,33 @@ def learn_once(depth_seq, trend_answer_score):
     i = torch.tensor(input_seq, dtype=torch.float)
     o = model(i)
 
-    loss = torch.square(o - trend_answer_score)
+    loss = abs(o - trend_answer_score)
     loss.backward(retain_graph=True)
     optimizer.step()
 
-    return loss.to_list()[0]
+    return float(loss)
 
 load_realtime_dataset_on_start()
-for i in range(300):
-    print("DEBUG: l=", realtime_shortterm_dataset_depth[i+256])
-    answer = realtime_shortterm_dataset_depth[i+256][1][0][0] - realtime_shortterm_dataset_depth[i][1][0][0]
+losses = []
+for i in range(2000):
+    answer = numpy.average([realtime_shortterm_dataset_depth[i+di][1][0][0] for di in (256,512,768,1024,2000,5000)]) - realtime_shortterm_dataset_depth[i][1][0][0]
     print("answer=", answer)
-    loss = learn_once(realtime_shortterm_dataset_depth[i], answer)
+    loss = learn_once(realtime_shortterm_dataset_depth[i:i+128], answer)
     print("Loss=", loss)
+    losses += [loss]
+
+#####################
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import numpy as np
+x = numpy.arange(len(losses))
+colors = cm.rainbow(np.linspace(0, 1, 10))
+for index, y in enumerate(losses):
+    plt.scatter(index, y, color=colors[1])
+#for x, y in guess_xy:
+#    plt.scatter(x, y, color=colors[6])
+plt.show()
+
+