#!/usr/bin/env python
"""Custom Indicators Module - User-defined indicators.
This module contains custom indicator algorithms including:
- MaBetweenHighAndLow: Check if MA is within price range
- BarsLast: Count bars since condition was met
- NewDiff: Guotai Junan alpha factor indicator
Example:
To use these custom indicators in your strategy::
import backtrader as bt
class MyStrategy(bt.Strategy):
def __init__(self):
# Create MA Between High and Low indicator
self.ma_hl = bt.indicators.MaBetweenHighAndLow(
self.data, period=5
)
# Create Bars Last indicator
self.bars_last = bt.indicators.BarsLast(
self.data, period=5
)
# Create NewDiff indicator (Guotai Junan alpha factor)
self.new_diff = bt.indicators.NewDiff(
self.data, period=6
)
def next(self):
# Use the indicators in your trading logic
if self.ma_hl.target[0] > 0:
# MA is within high-low range
self.buy()
if self.bars_last.bar_num[0] < 3:
# Condition was met less than 3 bars ago
self.sell()
# Access the alpha factor value
alpha_value = self.new_diff.factor[0]
"""
import math
import numpy as np
from . import SMA, If, Indicator, Max, Min
# This file contains some custom indicator algorithms
[docs]
class MaBetweenHighAndLow(Indicator):
"""Check if moving average is between high and low prices.
Returns 1.0 when SMA is within the bar's high-low range,
0.0 otherwise.
"""
# Check if moving average is between high and low prices
lines = ("target",)
params = (("period", 5),)
def __init__(self):
"""Initialize the MA Between High and Low indicator.
Creates SMA for comparison with high/low range.
"""
super().__init__()
self.ma = SMA(self.data.close, period=self.p.period)
[docs]
def next(self):
"""Check if MA is between high and low for current bar.
Returns 1.0 if MA is within range, 0.0 otherwise.
"""
ma_val = self.ma[0]
high_val = self.data.high[0]
low_val = self.data.low[0]
self.lines.target[0] = 1.0 if (ma_val < high_val and ma_val > low_val) else 0.0
[docs]
def once(self, start, end):
"""Check MA against high/low range in runonce mode.
Returns 1.0 where MA is within range, 0.0 otherwise.
"""
ma_array = self.ma.lines[0].array
high_array = self.data.high.array
low_array = self.data.low.array
larray = self.lines.target.array
while len(larray) < end:
larray.append(float("nan"))
for i in range(start, min(end, len(ma_array), len(high_array), len(low_array))):
ma_val = ma_array[i] if i < len(ma_array) else 0.0
high_val = high_array[i] if i < len(high_array) else 0.0
low_val = low_array[i] if i < len(low_array) else 0.0
if isinstance(ma_val, float) and math.isnan(ma_val):
larray[i] = float("nan")
else:
larray[i] = 1.0 if (ma_val < high_val and ma_val > low_val) else 0.0
[docs]
class BarsLast(Indicator):
"""Count bars since condition was last met.
Tracks the number of bars that have passed since a specified
condition (default: MaBetweenHighAndLow) was last true.
"""
# This indicator analyzes the number of bars since the last condition was met
lines = ("bar_num",)
params = (("period", 5), ("func", MaBetweenHighAndLow))
def __init__(self):
"""Initialize the Bars Last indicator.
Creates target function for condition checking.
"""
self.target = self.p.func(self.data, period=self.p.period)
self.num = np.nan
[docs]
def next(self):
"""Count bars since condition was last met.
Resets to 0 when condition is true, increments otherwise.
"""
if self.target[0]:
self.num = 0
self.lines.bar_num[0] = self.num
self.num = self.num + 1
[docs]
class NewDiff(Indicator):
"""Guotai Junan alpha factor indicator.
Calculates a proprietary alpha factor based on price movement
relative to previous close and the high/low range.
Formula:
SUM((CLOSE==DELAY(CLOSE,1)?0:CLOSE-(CLOSE>DELAY(CLOSE,1)?
MIN(LOW,DELAY(CLOSE,1)):MAX(HIGH,DELAY(CLOSE,1)))), period)
"""
# Indicator based on Guotai Junan alpha factor
# : SUM((CLOSE=DELAY(CLOSE,1)?0:CLOSE-(CLOSE>DELAY(CLOSE,1)?MIN(LOW,DELAY(CLOSE,1)):MAX(HIGH,DELAY(CLOSE,1)))),6)
# - e = MIN(LOW, DELAY(CLOSE, 1))
# - f = MAX(HIGH, DELAY(CLOSE, 1))
# - h = CLOSE > DELAY(CLOSE, 1)
# - b = h?e: f
# - a = CLOSE = DELAY(CLOSE, 1)?0: CLOSE - b
# - c = SUM(a, 6)
lines = ("factor",)
params = (("period", 5),)
def __init__(self):
"""Initialize the NewDiff indicator.
Sets up alpha factor calculation based on Guotai Junan formula.
"""
close = self.data.close
pre_close = self.data.close(-1)
e = Min(self.data.low, pre_close)
f = Max(self.data.high, pre_close)
b = If(close > pre_close, e, f)
self.a = If(close == pre_close, 0, close - b)
[docs]
def next(self):
"""Calculate NewDiff factor for the current bar.
Sums adjusted price differences over the period.
"""
if len(self.a) >= self.p.period:
self.lines.factor[0] = math.fsum(self.a.get(size=self.p.period))
else:
self.lines.factor[0] = np.nan