Hi,

To develop momentum strategies, its relevant to understand is the trend is strong or a mean reversion is coming. Tipically, Hurst Exponent claims to work, but unfortunally I dont see value on this indicator with my data (latinamerican mutual funds).

Finally I found the MMI as an alternative with better results to implement in my strategies. You can get the explanation and several backtestings on https://financial-hacker.com/the-market-meanness-index/ (ps: you can read there with 55 is important)

This is my implementation on backtrader, pls your feedback is welcome

class MMI(bt.Indicator):
    lines = ('mmi',)
    plotinfo = dict(plothlines=[55])
    params = dict(period=100,)

    def __init__(self):
        self.m = Average(self.data.close, period = self.p.period) #replace with median
        
    def next(self):
            nl = 0
            nh = 0

            for j in range(self.p.period-1):
                if j < 1:
                    continue
                i=j * -1
                if (self.data.close[i] > self.m[0]) and (self.data.close[i] > self.data.close[i-1]) :
                    nl += 1
                if (self.data.close[i] < self.m[0]) and (self.data.close[i] < self.data.close[i-1]) :
                    nh += 1
            self.l.mmi[0] = 100.0 * (nl+nh)/(self.p.period - 1)

This is my whole dark theme, I´ve tried to emulate TradingView schema.

plt.style.use('fivethirtyeight')

plt.rcParams["figure.figsize"] = (10, 6)
plt.rcParams['lines.linewidth'] = 1

SIZE = 7
plt.rcParams['axes.labelsize'] = SIZE
plt.rcParams['ytick.labelsize'] = SIZE
plt.rcParams['xtick.labelsize'] = SIZE
plt.rcParams["font.size"] = SIZE

COLOR = '1'
plt.rcParams['text.color'] = COLOR
plt.rcParams['axes.labelcolor'] = COLOR
plt.rcParams['xtick.color'] = COLOR
plt.rcParams['ytick.color'] = COLOR


plt.rcParams['grid.linewidth']=0.1
plt.rcParams['grid.color']="0.2"
plt.rcParams['lines.color']="0.5"
plt.rcParams['axes.edgecolor']="0.2"
plt.rcParams['axes.linewidth']=0.5

plt.rcParams['figure.facecolor']="#101622"
plt.rcParams['axes.facecolor']="#101622"
plt.rcParams["savefig.dpi"]=120
dpi = plt.rcParams["savefig.dpi"]
width = 700
height = 1200
plt.rcParams['figure.figsize'] = height/dpi, width/dpi
plt.rcParams["savefig.facecolor"] ="#101622"
plt.rcParams["savefig.edgecolor"]="#101622"

plt.rcParams['legend.fontsize'] = SIZE
plt.rcParams['legend.title_fontsize'] = SIZE + 1
plt.rcParams['legend.labelspacing'] =0.25
plt.rcParams['image.cmap']='tab10'

This is my dark color scheme simulating tradingview theme just adjusting general matplotlib defaults.

SIZE = 6
COLOR = 'white'
BACKGROUND = "#101622"
GRID="0.4"

def default_colors(color=COLOR, size=SIZE, background=BACKGROUND, grid=GRID):
    matplotlib.use('Agg')
    plt.style.use('fivethirtyeight')    
    plt.rcParams["figure.figsize"] = (10, 6)
    plt.rcParams['lines.linewidth'] = 0.5
    plt.rcParams['lines.color']="0.5"
    
    plt.rcParams["font.size"] = size
    plt.rcParams['axes.labelsize'] = size
    plt.rcParams['ytick.labelsize'] = size
    plt.rcParams['xtick.labelsize'] = size

    plt.rcParams['text.color'] = color
    plt.rcParams['axes.labelcolor'] = color
    plt.rcParams['xtick.color'] = color
    plt.rcParams['ytick.color'] = color
    
    plt.rcParams['axes.grid.axis']='both'
    plt.rcParams['grid.linewidth']=0.1
    plt.rcParams['grid.color']=grid
    #plt.rcParams['axes.edgecolor']="0.2"
    plt.rcParams['axes.linewidth']=0

   #plt.rcParams['grid.linewidth']=0
    
    plt.rcParams['figure.facecolor'] = background
    plt.rcParams['axes.facecolor'] = background
    plt.rcParams["savefig.dpi"]=120
    dpi = plt.rcParams["savefig.dpi"]
    width = 700
    height = 1200
    plt.rcParams['figure.figsize'] = height/dpi, width/dpi
    plt.rcParams["savefig.facecolor"] = background
    plt.rcParams["savefig.edgecolor"] = background
    
    plt.rcParams['legend.fontsize'] = SIZE + 2
    plt.rcParams['legend.title_fontsize'] = SIZE + 2
    plt.rcParams['legend.labelspacing'] = 0.25
    plt.rcParams['image.cmap']='tab10'
    
    plt.ioff()

Another trend indicator: ALMA
From http://www.financial-hacker.com/trend-delusion-or-reality/

Appreciate you feedback for bugs or omissions

Ps: "alma" means soul in spanish

class ALMA(bt.Indicator):
    lines = ('alma',)

    params = dict(
                    period=40,
                    sigma=6,
                    offset=1,
                )
    def __init__(self):

        self.asize = self.p.period - 1
        self.m = self.p.offset * self.asize
        self.s = self.p.period  / self.p.sigma
        self.dss = 2 * self.s * self.s

    def next(self):
        try:
            wtd_sum = 0
            self.l.alma[0] = 0
            if len(self) >= self.asize:
                for i in range(self.p.period):
                    im = i - self.m
                    wtd = np.exp( -(im * im) / self.dss)
                    self.l.alma[0] += self.data[0 - self.p.period + i] * wtd
                    wtd_sum += wtd
                self.l.alma[0] = self.l.alma[0] / wtd_sum
                print(self.l.alma[0])

        except TypeError:
            self.l.alma[0] = 0
            return

@Ender-Kina this my ALMA implementation as backtrader indicator

class ALMA(bt.Indicator):
    lines = ('alma',)

    params = dict(
                    period=40,
                    sigma=6,
                    offset=1,
                )
    
    '''
    ALMA - Arnaud Legoux Moving Average,
    http://www.financial-hacker.com/trend-delusion-or-reality/
    https://github.com/darwinsys/Trading_Strategies/blob/master/ML/Features.py
      ''' 
    def __init__(self):

        self.asize = self.p.period - 1
        self.m = self.p.offset * self.asize
        self.s = self.p.period  / self.p.sigma
        self.dss = 2 * self.s * self.s

    def next(self):
        try:
            wtd_sum = 0
            self.l.alma[0] = 0
            if len(self) >= self.asize:
                for i in range(self.p.period):
                    im = i - self.m
                    wtd = np.exp( -(im * im) / self.dss)
                    self.l.alma[0] += self.data[0 - self.p.period + i] * wtd
                    wtd_sum += wtd
                self.l.alma[0] = self.l.alma[0] / wtd_sum
                print(self.l.alma[0])

        except TypeError:
            self.l.alma[0] = 0
            return

Hi,

To develop momentum strategies, its relevant to understand is the trend is strong or a mean reversion is coming. Tipically, Hurst Exponent claims to work, but unfortunally I dont see value on this indicator with my data (latinamerican mutual funds).

Finally I found the MMI as an alternative with better results to implement in my strategies. You can get the explanation and several backtestings on https://financial-hacker.com/the-market-meanness-index/ (ps: you can read there with 55 is important)

This is my implementation on backtrader, pls your feedback is welcome

class MMI(bt.Indicator):
    lines = ('mmi',)
    plotinfo = dict(plothlines=[55])
    params = dict(period=100,)

    def __init__(self):
        self.m = Average(self.data.close, period = self.p.period) #replace with median
        
    def next(self):
            nl = 0
            nh = 0

            for j in range(self.p.period-1):
                if j < 1:
                    continue
                i=j * -1
                if (self.data.close[i] > self.m[0]) and (self.data.close[i] > self.data.close[i-1]) :
                    nl += 1
                if (self.data.close[i] < self.m[0]) and (self.data.close[i] < self.data.close[i-1]) :
                    nh += 1
            self.l.mmi[0] = 100.0 * (nl+nh)/(self.p.period - 1)

This is my whole dark theme, I´ve tried to emulate TradingView schema.

plt.style.use('fivethirtyeight')

plt.rcParams["figure.figsize"] = (10, 6)
plt.rcParams['lines.linewidth'] = 1

SIZE = 7
plt.rcParams['axes.labelsize'] = SIZE
plt.rcParams['ytick.labelsize'] = SIZE
plt.rcParams['xtick.labelsize'] = SIZE
plt.rcParams["font.size"] = SIZE

COLOR = '1'
plt.rcParams['text.color'] = COLOR
plt.rcParams['axes.labelcolor'] = COLOR
plt.rcParams['xtick.color'] = COLOR
plt.rcParams['ytick.color'] = COLOR


plt.rcParams['grid.linewidth']=0.1
plt.rcParams['grid.color']="0.2"
plt.rcParams['lines.color']="0.5"
plt.rcParams['axes.edgecolor']="0.2"
plt.rcParams['axes.linewidth']=0.5

plt.rcParams['figure.facecolor']="#101622"
plt.rcParams['axes.facecolor']="#101622"
plt.rcParams["savefig.dpi"]=120
dpi = plt.rcParams["savefig.dpi"]
width = 700
height = 1200
plt.rcParams['figure.figsize'] = height/dpi, width/dpi
plt.rcParams["savefig.facecolor"] ="#101622"
plt.rcParams["savefig.edgecolor"]="#101622"

plt.rcParams['legend.fontsize'] = SIZE
plt.rcParams['legend.title_fontsize'] = SIZE + 1
plt.rcParams['legend.labelspacing'] =0.25
plt.rcParams['image.cmap']='tab10'

Hi,

Did you solve that? Im trying to backtest mutual funds and need to simulate this behavior, typycally the mutual funds admin spend 3/4 days to make a change here in latin america.

So I decided to implement this logic into any strategy code but open to recieve some feedback in order to have a better implementation. I´d rather to put this logic into cerebro/order management.

def nextstart(self):
    self.order= None
    self.diacambio = 0
    self.fondo="In"
    self.buy(self.data0)   
      
def __init__(self, **kwargs):  
    self.__dict__.update(kwargs)
    super().__init__()
    self.signal_buy = [False]
    self.signal_sell = [False]
    self.data0_emarapida = EMA(self.data0.close,period=self.p.emarapida,plotname="FastEMAPLOT", plot=False)
    self.data0_emalenta = EMA(self.data0.close,period=self.p.emalenta,plotname="SlowEMA", plot=False)
    self.crossoverEMA = CrossOver(self.data0_emalenta, self.data0_emarapida, plotname="CrossOver", plot=False)

def next(self):       
    self.diacambio = self.diacambio + 1
    
    if self.crossoverEMA[0] > 0 and self.fondo == "Out":
        self.signal_buy[0] = True
    if self.crossoverEMA[0] < 0 and self.fondo == "In":
        self.signal_sell[0] = True

    if self.diacambio >= self.p.delayed:
        if self.fondo == "Order Buy":
                    self.order=self.buy(data=self.data0) 
                    #self.log("BUY EXECUTED " + self.data0._name)
                    self.fondo ="In"
                    self.signal_buy[0] = False
                    return
        elif self.fondo == "Order Sell":
                    self.order=self.sell(data=self.data0) 
                    #self.log("SELL EXECUTED" + self.data0._name)
                    self.fondo ="Out"
                    self.signal_sell[0] = False
                    return

    if self.fondo == "Out":
            if self.signal_buy[0] == True :
                    self.log("BUY  " + self.data0._name)
                    self.fondo ="Order Buy"
                    self.diacambio = 0
                    return
    elif self.fondo == "In":
            if self.signal_sell[0] == True :
                    self.log("SELL " + self.data0._name)
                    self.fondo ="Order Sell"
                    self.diacambio = 0
                    return