Code & Readme Improvements

README.md

@@ -68,6 +68,7 @@
 
 ### Data Explorations
 
+0. Downloader usage: `python downloader_crypto.py bitmex BTC/USD 2015-01-01T00:00:00Z 1d btc.csv`
 1. stock market study on TESLA stock, [tesla-study.ipynb](misc/tesla-study.ipynb)
 2. fashion trending prediction with cross-validation, [fashion-forecasting.ipynb](misc/fashion-forecasting.ipynb)
 3. Bitcoin analysis with LSTM prediction, [bitcoin-analysis-lstm.ipynb](misc/bitcoin-analysis-lstm.ipynb)

agent/Evolution_Bayesian.py → agent-new/Evolution_Bayesian.py

@@ -5,8 +5,14 @@
 import seaborn as sns
 import random
 from bayes_opt import BayesianOptimization
+import os, sys
+sys.path.insert(1, os.path.join(sys.path[0], '../dataset'))
+from crypto_data_loader import get_candles
 sns.set()
 
+use_csv = False
+
+
 def get_state(data, t, n):
     d = t - n + 1
     block = data[d : t + 1] if d >= 0 else -d * [data[0]] + data[0 : t + 1]
@@ -15,15 +21,6 @@ def get_state(data, t, n):
         res.append(block[i + 1] - block[i])
     return np.array([res])
 
-df = pd.read_csv('../dataset/GOOG-year.csv')
-print(df.tail())
-
-close = df.Close.values.tolist()
-window_size = 30
-skip = 5
-l = len(close) - 1
-
-
 class Deep_Evolution_Strategy:
 
     inputs = None
@@ -291,20 +288,41 @@ def find_best_agent(
     if investment > accbest:
         costbest = investment
     return investment
+##--------------------------------------------------
+##Settings (Later as argv)
+exchange = 'bitmex'
+symbol = 'BTC/USD'
+start_date = '2018-01-01T00:00:00Z'
+timeframe = '1d'
+
+#---------------------------------------------------
+##Get Data
+if use_csv:
+    df = pd.read_csv('../dataset/btcusd.csv')
+else:
+    df = get_candles(exchange , 3 , symbol ,timeframe , start_date ,25) #df = pd.read_csv('../dataset/GOOG-year.csv')
+print(df)
+
+close = df.Close.values.tolist()
+window_size = 30
+skip = 5
+l = len(close) - 1
 
+##----------------------------------------------------
+## Bayesian Stuff
 accbest = 0.0
 NN_BAYESIAN = BayesianOptimization(
     find_best_agent,
     {
-        'window_size': (2, 50),#2,50
-        'skip': (1, 15), #1,15
-        'population_size': (1, 50),#1,50
+        'window_size': (2, 50),#standard: 2,50
+        'skip': (1, 15), #standard: 1,15
+        'population_size': (1, 50),#standard: 1,50
         'sigma': (0.01, 0.99),
-        'learning_rate': (0.000001, 0.49),#0.000001 , 0.49
-        'size_network': (10, 1000),#10,1000
+        'learning_rate': (0.000001, 0.49),#standard: 0.000001 , 0.49
+        'size_network': (10, 1000),#standard: 10,1000
     },
 )
-NN_BAYESIAN.maximize(init_points = 50, n_iter = 200, acq = 'ei', xi = 0.0)#n_iter=50 init_points=30
+NN_BAYESIAN.maximize(init_points = 50, n_iter = 80, acq = 'ei', xi = 0.0)#standard: init_points=30 n_iter=50
 
 
 print('----------------------------------------------')
@@ -321,6 +339,6 @@ def find_best_agent(
 model = Model(int(params['window_size']) , int(params['size_network']) ,3)
 agent = Agent(int(params['population_size']) , params['sigma'] , params['learning_rate'] , model , 10000 , 5 , 5, int(params['skip']),int(params['window_size']))
 
-agent.fit(500, 100)
+agent.fit(500, 50)
 
 agent.buy()

agent-new/Pipfile

@@ -0,0 +1,18 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+pprint = "*"
+numpy = "*"
+pandas = "*"
+seaborn = "*"
+bayesian-optimization = "*"
+matplotlib = "*"
+ccxt = "*"
+
+[requires]
+python_version = "3.7"

agent/Pipfile

@@ -0,0 +1,13 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+ccxt = "*"
+pprint = "*"
+
+[requires]
+python_version = "3.7"

agent/README.md

@@ -0,0 +1,22 @@
+Using Pipenv with the Pipfile I included in this directory will get you the proper bayesian_optimization package (For the original version of the /agent scripts) as well as jupyter notebook kernel to work with it. The post-1.0 version of bayesian_optimization requires different code. All to be upgrdaed. This is just to run the original code. Commands to get started:
+
+**pipenv --python 3.7.2**
+
+**pipenv shell**
+
+**pipenv install ipykernel**
+
+**pipenv sync**
+
+**python -m ipykernel install --user --name=my-virtualenv-name**
+
+
+Replace "my-virtualenv-name" with the name of your pipenv environment. You can see it in the path of the command:
+
+**which python**
+
+Then you can:
+
+**jupyter notebook --ip=127.0.0.1**
+
+Don't forget to switch to the custom agent kernel once the notebook opens.