Merge branch 'master' of https://github.com/jesse-ai/jesse

jesse/helpers.py

@@ -1034,21 +1034,12 @@ def get_candle_start_timestamp_based_on_timeframe(timeframe: str, num_candles_to
     return finish_date - (num_candles_to_fetch * one_min_count * 60_000)
 
 
-def is_price_near(order_price, price_to_compare):
+def is_price_near(order_price, price_to_compare, percentage_threshold=0.0001):
     """
-    Check if given order price is near the specified price.
-    This function checks a range of price values and applies the associated threshold.
-
-    :param order_price: float
-    :param price_to_compare: float
-    :return: bool
+    Check if the given order price is near the specified price.
+    Default percentage_threshold is 0.01% (0.0001)
+    We calculate percentage difference between the two prices rounded to 4 decimal places, 
+    so low-priced orders can be properly compared within 0.01% range.
     """
-    # Define the price ranges and associated thresholds
-    conditions = [order_price < 0.01, order_price < 1, order_price < 100, order_price < 10000, order_price >= 10000]
-    threshold_ratios = [0.015, 0.01, 0.005, 0.001, 0.0001]
-
-    # Use np.select to choose the threshold ratio based on the conditions
-    threshold_ratio = np.select(conditions, threshold_ratios)
+    return round(abs(1 - (order_price / price_to_compare)), 4) <= percentage_threshold
 
-    threshold = threshold_ratio * order_price
-    return abs(order_price - price_to_compare) <= threshold

jesse/indicators/ma.py

@@ -17,6 +17,48 @@ def ma(candles: np.ndarray, period: int = 30, matype: int = 0,  source_type: str
     :param sequential: bool - default: False
 
     :return: float | np.ndarray
+
+    0: sma (simple)
+    1: ema (exponential)
+    2: wma (weighted)
+    3: dema (double exponential)
+    4: tema (triple exponential)
+    5: trima (triangular)
+    6: kama (Kaufman adaptive)
+    7: mama (Mesa adaptive)
+    8: T3 (triple exponential T3)
+    9: fwma (Fibonacci's Weighted Moving Average)
+    10: hma (Hull Moving Average)
+    11: linearreg (Linear Regression)
+    12: wilders (Wilders Smoothing)
+    13: sinwma (Sine Weighted Moving Average)
+    14: supersmoother (Super Smoother Filter 2pole Butterworth)
+    15: supersmoother\_3\_pole(Super Smoother Filter 3pole Butterworth)
+    16: gauss (Gaussian Filter)
+    17: high\_pass (1-pole High Pass Filter by John F. Ehlers)
+    18: high\_pass\_2\_pole (2-pole High Pass Filter by John F. Ehlers)
+    19: ht\_trendline (Hilbert Transform - Instantaneous Trendline)
+    20: jma (Jurik Moving Average)
+    21: reflex (Reflex indicator by John F. Ehlers)
+    22: trendflex (Trendflex indicator by John F. Ehlers)
+    23: smma (Smoothed Moving Average)
+    24: vwma (Volume Weighted Moving Average)
+    25: pwma (Pascals Weighted Moving Average)
+    26: swma (Symmetric Weighted Moving Average)
+    27: alma (Arnaud Legoux Moving Average)
+    28: hwma (Holt-Winter Moving Average)
+    29: vwap (Volume weighted average price)
+    30: nma (Natural Moving Average)
+    31: edcf (Ehlers Distance Coefficient Filter)
+    32: mwdx (MWDX Average)
+    33: maaq (Moving Average Adaptive Q)
+    34: srwma (Square Root Weighted Moving Average)
+    35: sqwma (Square Weighted Moving Average)
+    36: vpwma (Variable Power Weighted Moving Average)
+    37: cwma (Cubed Weighted Moving Average)
+    38: jsa (Jsa Moving Average)
+    39: epma (End Point Moving Average)
+
     """
 
     candles = slice_candles(candles, sequential)

jesse/modes/optimize_mode/Optimize.py

@@ -206,7 +206,7 @@ def evolve(self) -> list:
             self.generate_initial_population()
 
             if len(self.population) < 0.5 * self.population_size:
-                msg = f'Too many errors! less than half of the expected population size could be generated. Only {len(self.population)} indviduals from planned {self.population_size} are usable.'
+                msg = f'Too many errors! less than half of the expected population size could be generated. Only {len(self.population)} individuals from planned {self.population_size} are usable.'
                 logger.log_optimize_mode(msg)
                 raise ValueError(msg)
 

jesse/version.py

@@ -1 +1 @@
-__version__ = '0.46.0'
+__version__ = '0.46.1'

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 # also change in version.py
-VERSION = '0.46.0'
+VERSION = '0.46.1'
 DESCRIPTION = "A trading framework for cryptocurrencies"
 with open("requirements.txt", "r", encoding="utf-8") as f:
     REQUIRED_PACKAGES = f.read().splitlines()

tests/test_broker.py

@@ -160,7 +160,7 @@ def test_stop_loss():
     assert position.entry_price == 50
     assert position.qty == 1
     assert exchange.available_margin == 950
-    # even executed orders should not affect wallet_balance unless it's for reducing positon
+    # even executed orders should not affect wallet_balance unless it's for reducing position
     assert exchange.wallet_balance == 1000
 
     order = broker.reduce_position_at(1, 40, 50)

tests/test_helpers.py

@@ -648,7 +648,8 @@ def test_round_or_none():
 
 def test_is_price_near():
     assert jh.is_price_near(0.007386, 0.007385) == True
-    assert jh.is_price_near(0.007386, 0.007396) == True
+    assert jh.is_price_near(0.007386, 0.007396) == False
+    assert jh.is_price_near(0.0250, 0.0249) == False
     assert jh.is_price_near(60000, 60000) == True
     assert jh.is_price_near(60000, 60000.1) == True
     assert jh.is_price_near(60000, 60100) == False