Merge branch 'develop' into plot_profit

scripts/plot_dataframe.py

@@ -3,14 +3,23 @@
 import sys
 import logging
 import argparse
+
 import matplotlib
+# matplotlib.use("Qt5Agg")
 import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
+from pandas import DataFrame
+import talib.abstract as ta
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade import exchange, analyze
+from freqtrade.misc import common_args_parser
+from freqtrade.strategy.strategy import Strategy
 import freqtrade.misc as misc
 import freqtrade.optimize as optimize
 import freqtrade.analyze as analyze
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -21,7 +30,7 @@ def plot_parse_args(args):
     return parser.parse_args(args)
 
 
-def plot_analyzed_dataframe(args):
+def plot_analyzed_dataframe(args) -> None:
     """
     Calls analyze() and plots the returned dataframe
     :param pair: pair as str
@@ -31,35 +40,40 @@ def plot_analyzed_dataframe(args):
     pairs = [pair]
     timerange = misc.parse_timerange(args.timerange)
 
+    # Init strategy
+    strategy = Strategy()
+    strategy.init({'strategy': args.strategy})
+    tick_interval = strategy.ticker_interval
+
     tickers = {}
     if args.live:
         logger.info('Downloading pair.')
+        # Init Bittrex to use public API
         exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
-        tickers[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
+        tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
     else:
         tickers = optimize.load_data(args.datadir, pairs=pairs,
-                                     ticker_interval=args.ticker_interval,
+                                     ticker_interval=tick_interval,
                                      refresh_pairs=False,
                                      timerange=timerange)
     dataframes = optimize.tickerdata_to_dataframe(tickers)
     dataframe = dataframes[pair]
     dataframe = analyze.populate_buy_trend(dataframe)
     dataframe = analyze.populate_sell_trend(dataframe)
-
     dates = misc.datesarray_to_datetimearray(dataframe['date'])
 
-    dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
-    dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
-
     # Two subplots sharing x axis
     fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
-    fig.suptitle(pair + " " + str(args.ticker_interval), fontsize=14, fontweight='bold')
+    fig.suptitle(pair + " " +     str(tick_interval), fontsize=14, fontweight='bold')
+
     ax1.plot(dates, dataframe['close'], label='close')
     # ax1.plot(dates, dataframe['sell'], 'ro', label='sell')
     ax1.plot(dates, dataframe['sma'], '--', label='SMA')
     ax1.plot(dates, dataframe['tema'], ':', label='TEMA')
     ax1.plot(dates, dataframe['blower'], '-.', label='BB low')
-    ax1.plot(dates, dataframe['buy_price'], 'bo', label='buy')
+    ax1.plot(dates, dataframe['close'] * dataframe['buy'], 'bo', label='buy')
+    ax1.plot(dates, dataframe['close'] * dataframe['sell'], 'ro', label='sell')
+
     ax1.legend()
 
     ax2.plot(dates, dataframe['adx'], label='ADX')
@@ -81,7 +95,6 @@ def plot_analyzed_dataframe(args):
     plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
     plt.show()
 
-
 if __name__ == '__main__':
     args = plot_parse_args(sys.argv[1:])
     plot_analyzed_dataframe(args)

scripts/plot_profit.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
 
 import sys
-import argparse
 import json
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
@@ -10,6 +9,7 @@ import numpy as np
 import freqtrade.optimize as optimize
 import freqtrade.misc as misc
 import freqtrade.exchange as exchange
+from freqtrade.strategy.strategy import Strategy
 
 
 def plot_parse_args(args):
@@ -44,7 +44,7 @@ def make_profit_array(data, px, filter_pairs=[]):
     # total profits at each timeframe
     # to accumulated profits
     pa = 0
-    for x in range(0,len(pg)):
+    for x in range(0, len(pg)):
         p = pg[x]  # Get current total percent
         pa += p  # Add to the accumulated percent
         pg[x] = pa  # write back to save memory
@@ -67,7 +67,14 @@ def plot_profit(args) -> None:
     filter_pairs = args.pair
 
     config = misc.load_config(args.config)
+    config.update({'strategy': args.strategy})
+
+    # Init strategy
+    strategy = Strategy()
+    strategy.init(config)
+
     pairs = config['exchange']['pair_whitelist']
+
     if filter_pairs:
         filter_pairs = filter_pairs.split(',')
         pairs = list(set(pairs) & set(filter_pairs))
@@ -75,7 +82,7 @@ def plot_profit(args) -> None:
 
     timerange = misc.parse_timerange(args.timerange)
     tickers = optimize.load_data(args.datadir, pairs=pairs,
-                                 ticker_interval=args.ticker_interval,
+                                 ticker_interval=strategy.ticker_interval,
                                  refresh_pairs=False,
                                  timerange=timerange)
     dataframes = optimize.preprocess(tickers)
@@ -96,7 +103,7 @@ def plot_profit(args) -> None:
     for pair, pair_data in dataframes.items():
         close = pair_data['close']
         maxprice = max(close)  # Normalize price to [0,1]
-        print('Pair %s has length %s' %(pair, len(close)))
+        print('Pair %s has length %s' % (pair, len(close)))
         for x in range(0, len(close)):
             avgclose[x] += close[x] / maxprice
         # avgclose += close
@@ -108,7 +115,7 @@ def plot_profit(args) -> None:
 
     filename = 'backtest-result.json'
     with open(filename) as file:
-      data = json.load(file)
+        data = json.load(file)
     pg = make_profit_array(data, max_x, filter_pairs)
 
     #