first commit

jeroen_test.py

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+
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from functools import reduce
+from pandas import DataFrame
+from freqtrade.persistence import Trade
+from datetime import datetime, date, timedelta
+
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy  # noqa
+import logging
+
+logger = logging.getLogger(__name__)
+
+class jeroen_test(IStrategy):
+
+    # Minimal ROI designed for the strategy.
+    minimal_roi = {
+        "0": 0.02
+    }
+
+    order_types = {
+        'buy': 'market',
+        'sell': 'market',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -10
+
+    # Optimal timeframe for the strategy
+    timeframe = '1m'
+
+    def calc_profit(self, price: float, current: float) -> float:
+        fee = 1.0007
+        profit = ((current*fee) -
+            (price*fee))
+
+        return float(f"{profit:.8f}")
+
+    def calc_percentage_lower(self, price: float, current: float) -> float:
+        fee = 1.0007
+        price = price*fee
+        current = current*fee
+        lowerpercent = ((price-current)/(price*fee))*100
+
+        return float(f"{lowerpercent:.8f}")
+
+    def bot_loop_start(self, **kwargs) -> None:
+        print(" ")
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """ Adds several different TA indicators to the given DataFrame
+        """
+        profit = False
+        profit_percent = False
+        percent_lower = False
+        current_price = dataframe['close'].iloc[-1]
+
+        dataframe['should_sell'] = False
+        dataframe['should_buy'] = False
+
+        # Get the previous trade
+        trade = Trade.get_trades_proxy(is_open=False, pair=metadata['pair'])
+        if trade:
+            trade = trade[-1]
+            lsp = trade.close_rate
+            if lsp:
+                percent_lower = self.calc_percentage_lower(price=lsp, current=current_price)
+            # Found a bug? When force selling it doesnt close it
+            else:
+                lsp = trade.open_rate
+                if lsp:
+                    percent_lower = self.calc_percentage_lower(price=lsp, current=current_price)
+        else:
+            lsp = 0.00
+
+        # Get the current Trade
+        trade = Trade.get_trades_proxy(is_open=True, pair=metadata['pair'])
+        if trade:
+            trade = trade[-1]
+            lbp = trade.open_rate
+            open_trade = True
+            profit = self.calc_profit(price=lbp, current=current_price)
+            profit_percent = (profit/lbp)*100
+        else:
+            lbp = 0.00
+            open_trade = False
+            profit = False
+            profit_percent = False
+
+
+        print("------------")
+
+        print("Last Sold For:", lsp)
+
+        if open_trade:
+            print("Bought for: ", lbp)
+        print("Current Price: ", current_price)
+        if profit:
+            print("Current Profit: ", profit, " ", float(f"{profit_percent:.8f}"), "%")
+        if percent_lower and not open_trade:
+            print("Percent Lower: ", float(f"{percent_lower:.8f}"), "%")
+
+
+        # Should we Sell?
+        if profit_percent:
+            if profit_percent > 1:
+                dataframe['should_sell'] = True
+
+        # Should we buy?
+        if not open_trade:
+            if (lsp == 0.00 ) & (lbp == 0.00):
+                dataframe['should_buy'] = True
+            # Is the percentage of what we sold for and the current price 2% lower
+            if percent_lower > 2:
+                dataframe['should_buy'] = True
+
+
+        dataframe['last_sell_price'] = lsp
+        dataframe['last_buy_price'] = lbp
+
+
+        print("Current Dataframe:")
+        print(dataframe.tail(1))
+
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[(
+            # We have not bought or sold anything yet, lets buy!
+            ((dataframe['last_sell_price'] == 0.00) & (dataframe['last_buy_price'] == 0.00) ) |
+            (
+                # Make sure the last selling price is higher than the current price
+                ((dataframe['last_sell_price']) > dataframe['close']) &
+
+                # Calculated earlier
+                (dataframe['should_buy'] == True)
+            )
+            ), 'buy'
+        ] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[(
+                # Make at least profit
+                (dataframe['last_buy_price'] < (dataframe['close'])) &
+
+                # Calculated earlier
+                (dataframe['should_sell'] == True) &
+
+                # If we have nothing we bought, there is nothing to sell
+                (dataframe['last_buy_price'] > 0.00)
+            ), 'sell'
+        ] = 1
+
+        return dataframe