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'

@jason-1 based on my IB and ib_insync knowledge, this is a setup into IB, no impact on order fullfilment, as this piece of code try to solve.

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