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wtc.py

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+# WTC Strategy: WTC(World Trade Center Tabriz)
+# is the biggest skyscraper of Tabriz, city of Iran
+# (What you want?it not enough for you?that's just it!)
+# No, no, I'm kidding. It's also mean Wave Trend with Crosses
+# algo by LazyBare(in TradingView) that I reduce it
+# signals noise with dividing it to Stoch-RSI indicator.
+# Also thanks from discord: @aurax for his/him
+# request to making this strategy.
+# hope you enjoy and get profit
+# Author: @Mablue (Masoud Azizi)
+# IMPORTANT: install sklearn befoure you run this strategy:
+# pip install sklearn
+# github: https://github.com/mablue/
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell --strategy wtc
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import talib.abstract as ta
+from freqtrade.strategy import DecimalParameter
+from freqtrade.strategy import IStrategy
+from pandas import DataFrame
+#
+# --- Do not remove these libs ---
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from sklearn import preprocessing
+
+# --------------------------------
+# Add your lib to import here
+
+
+class wtc(IStrategy):
+    ################################ SETTINGS ################################
+    # 61 trades. 16/0/45 Wins/Draws/Losses.
+    # * Avg profit: 132.53%.
+    # Median profit: -12.97%.
+    # Total profit: 0.80921449 BTC ( 809.21Σ%).
+    # Avg duration 4 days, 7:47:00 min.
+    # Objective: -15.73417
+
+    # Config:
+    # "max_open_trades": 10,
+    # "stake_currency": "BTC",
+    # "stake_amount": 0.01,
+    # "tradable_balance_ratio": 0.99,
+    # "timeframe": "30m",
+    # "dry_run_wallet": 0.1,
+
+    # Buy hyperspace params:
+    buy_params = {
+        "buy_max": 0.9609,
+        "buy_max0": 0.8633,
+        "buy_max1": 0.9133,
+        "buy_min": 0.0019,
+        "buy_min0": 0.0102,
+        "buy_min1": 0.6864,
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        "sell_max": -0.7979,
+        "sell_max0": 0.82,
+        "sell_max1": 0.9821,
+        "sell_min": -0.5377,
+        "sell_min0": 0.0628,
+        "sell_min1": 0.4461,
+    }
+    minimal_roi = {
+        "0": 0.030873,
+        "569": 0.016689,
+        "3211": 0.006473,
+        "7617": 0
+    }
+    stoploss = -0.128
+    ############################## END SETTINGS ##############################
+    timeframe = '5m'
+
+    buy_max = DecimalParameter(-1, 1, decimals=4, default=0.4393, space='buy')
+    buy_min = DecimalParameter(-1, 1, decimals=4, default=-0.4676, space='buy')
+    sell_max = DecimalParameter(-1, 1, decimals=4,
+                                default=-0.9512, space='sell')
+    sell_min = DecimalParameter(-1, 1, decimals=4,
+                                default=0.6519, space='sell')
+
+    buy_max0 = DecimalParameter(0, 1, decimals=4, default=0.4393, space='buy')
+    buy_min0 = DecimalParameter(0, 1, decimals=4, default=-0.4676, space='buy')
+    sell_max0 = DecimalParameter(
+        0, 1, decimals=4, default=-0.9512, space='sell')
+    sell_min0 = DecimalParameter(
+        0, 1, decimals=4, default=0.6519, space='sell')
+
+    buy_max1 = DecimalParameter(0, 1, decimals=4, default=0.4393, space='buy')
+    buy_min1 = DecimalParameter(0, 1, decimals=4, default=-0.4676, space='buy')
+    sell_max1 = DecimalParameter(
+        0, 1, decimals=4, default=-0.9512, space='sell')
+    sell_min1 = DecimalParameter(
+        0, 1, decimals=4, default=0.6519, space='sell')
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # WAVETREND
+        try:
+            ap = (dataframe['high']+dataframe['low'] + dataframe['close'])/3
+
+            esa = ta.EMA(ap, 10)
+
+            d = ta.EMA((ap - esa).abs(), 10)
+            ci = (ap - esa).div(0.0015 * d)
+            tci = ta.EMA(ci, 21)
+
+            wt1 = tci
+            wt2 = ta.SMA(np.nan_to_num(wt1), 4)
+
+            dataframe['wt1'], dataframe['wt2'] = wt1, wt2
+
+            stoch = ta.STOCH(dataframe, 14)
+            slowk = stoch['slowk']
+            dataframe['slowk'] = slowk
+            # print(dataframe.iloc[:, 6:].keys())
+            x = dataframe.iloc[:, 6:].values  # returns a numpy array
+            min_max_scaler = preprocessing.MinMaxScaler()
+            x_scaled = min_max_scaler.fit_transform(x)
+            dataframe.iloc[:, 6:] = pd.DataFrame(x_scaled)
+            # print('wt:\t', dataframe['wt'].min(), dataframe['wt'].max())
+            # print('stoch:\t', dataframe['stoch'].min(), dataframe['stoch'].max())
+            dataframe['def'] = dataframe['slowk']-dataframe['wt1']
+            # print('def:\t', dataframe['def'].min(), "\t", dataframe['def'].max())
+        except:
+            dataframe['wt1'], dataframe['wt2'], dataframe['def'], dataframe['slowk'] = 0, 10, 100, 1000
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['wt1'], dataframe['wt2']))
+                & (dataframe['wt1'].between(self.buy_min0.value, self.buy_max0.value))
+                & (dataframe['slowk'].between(self.buy_min1.value, self.buy_max1.value))
+                & (dataframe['def'].between(self.buy_min.value, self.buy_max.value))
+
+            ),
+
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # print(dataframe['slowk']/dataframe['wt1'])
+        # dataframe.loc[
+        #     (
+        #         (qtpylib.crossed_below(dataframe['wt1'], dataframe['wt2']))
+        #         & (dataframe['wt1'].between(self.sell_min0.value, self.sell_max0.value))
+        #         & (dataframe['slowk'].between(self.sell_min1.value, self.sell_max1.value))
+        #         & (dataframe['def'].between(self.sell_min.value, self.sell_max.value))
+        #
+        #     ),
+        #     'sell'] = 1
+        return dataframe