Merge pull request 'thibaudlabat' (#1) from thibaud into master

Reviewed-on: #1

.gitignore

@@ -225,3 +225,5 @@ fabric.properties
 # Android studio 3.1+ serialized cache file
 .idea/caches/build_file_checksums.ser
 
+# JetBrains Tools
+.idea/

auto_trading/main.py

@@ -4,7 +4,6 @@
 """
 import pandas as pd
 
-
 from .interfaces import Portfolio, Strategy, Broker, Predictor
 
 
@@ -17,6 +16,8 @@ class Bot:
         self.strategy = strategy
         self.broker = broker
         self.predictor = predictor
+        self._current_conversion_rate = None
+        self._balance = 0  # USD$
 
     def run(self):
         """run the bot"""
@@ -26,10 +27,16 @@ class Bot:
     def run_once(self):
         """run the bot once"""
         data = self.broker.next()
-        current_conversion_rate = data.iloc[-1].fillna(0).to_dict()
-        self.strategy.run(self.ptf, self.predictor.predict(data), current_conversion_rate)
+        self._current_conversion_rate = data.iloc[-1].fillna(0).to_dict()
+        self.strategy.run(self.ptf, self.predictor.predict(data), self._current_conversion_rate)
 
     def print_results(self):
         """print the results"""
-        for k,v in self.ptf.content().items():
+        for k, v in self.ptf.content().items():
             print(f"{k}: {v:e}")
+
+    def balance(self):
+        money = 0
+        for k, v in self.ptf.content().items():
+            money += self._current_conversion_rate[k] * v
+        return money

auto_trading/strat/all_in.py

@@ -1,4 +1,5 @@
 from ..interfaces import Strategy, Portfolio
+import numpy as np
 
 class AllIn(Strategy):
     """Just all in the greatest result"""
@@ -17,7 +18,9 @@ class AllIn(Strategy):
                 ptf.widraw(amount, stock)
         
         # after get the greatest result
-        greatest = max(result, key=lambda k: result[k])
+        result = {k:-1 if np.isnan(v) else v for k,v in result.items() }
+        epsilon = 1e-6
+        greatest = max(result, key=lambda k: result[k]-(np.inf if current_conversion_rate[k]<epsilon else 0))
         
         # then buy all the greatest result
         to_add = money/current_conversion_rate[greatest]

main.py

@@ -5,13 +5,14 @@ from auto_trading.predictor.mean_agg import MeanAggregator
 from auto_trading.predictor.normalized import NormalizedPredictor
 from auto_trading.predictor.selector import SelectorPredictor
 from auto_trading.main import Bot
+import matplotlib.pyplot as plt
 
 if __name__ == '__main__':
     csv = "data/price_history.csv"
     with open(csv, 'r') as f:
         head = f.readline().replace("\n", "").split(",")[1:]
     
-    pred = MeanAggregator([NormalizedPredictor(), SelectorPredictor({"Tether": 0.7})])
+    pred = MeanAggregator([NormalizedPredictor(),SelectorPredictor({"Tether": 0.7})])
     
     bot = Bot(
         ptf=InMemoryPortfolio({k:1 for k in head}),
@@ -22,8 +23,18 @@ if __name__ == '__main__':
     
     bot.print_results()
     print("\n")
-    
-    for _ in range(3):
+
+    TIME = range(400)
+    B = []
+    for _ in TIME:
         bot.run_once()
-        bot.print_results()
+        #bot.print_results()
+        assets = bot.ptf.content()
+        spread=1/100
+        for k in assets.keys():
+            assets[k]=(1-spread)*assets[k]
+        B.append( bot.balance() )
+        print(B[-1])
         print("\n")
+    plt.plot(TIME,B)
+    plt.show()

requirements.txt

@@ -0,0 +1,3 @@
+matplotlib
+numpy
+pandas