From e76cae7bbb9529eeb6ff0704f3eb507a78638ffb Mon Sep 17 00:00:00 2001
From: girdakan <mablue92@gmail.com>
Date: Fri, 5 Nov 2021 20:55:59 +0000
Subject: [PATCH] Khayyam strategy added

---
 user_data/strategies/Khayyam.py | 60 +++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 user_data/strategies/Khayyam.py

diff --git a/user_data/strategies/Khayyam.py b/user_data/strategies/Khayyam.py
new file mode 100644
index 0000000..05a9885
--- /dev/null
+++ b/user_data/strategies/Khayyam.py
@@ -0,0 +1,60 @@
+# Khayyam Strategy
+# In this strategy we try to find the best hours to buy and sell in a day.(in hourly timeframe)
+# Cuz of that you should just use 1h timeframe on this strategy.
+# Name of this strategy come from the Omar khayyam who was a Persian polymath, 
+# mathematician, astronomer, philosopher, and poet! 
+# https://en.wikipedia.org/wiki/Omar_Khayyam
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy Khayyam
+
+from freqtrade.strategy.hyper import IntParameter
+from freqtrade.strategy import IStrategy
+from pandas import DataFrame
+# --------------------------------
+# Add your lib to import here
+
+
+class Khayyam(IStrategy):
+    # ROI table:
+    minimal_roi = {
+        "0": 0.434,
+        "391": 0.116,
+        "511": 0.025,
+        "1919": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.29
+
+    # Optimal timeframe
+    timeframe = '1h'
+
+    buy_hour_max = IntParameter(0, 24, default=24, space='buy')
+    buy_hour_min = IntParameter(0, 24, default=11, space='buy')
+
+    sell_hour_max = IntParameter(0, 24, default=6, space='sell')
+    sell_hour_min = IntParameter(0, 24, default=15, space='sell')
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                dataframe['date'].apply(lambda x: x.hour).between(
+                    self.buy_hour_min.value, self.buy_hour_max.value)
+            ),
+
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                dataframe['date'].apply(lambda x: x.hour).between(
+                    self.sell_hour_min.value, self.sell_hour_max.value)
+            ),
+            'sell'] = 1
+        return dataframe