Algo: TMA2

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Algo: TMA2

Last comment by - September 2019

Algo: TMA2

Algo TMA2: Mean reversion variant that uses triangular moving average of candle averages. Find suitable stocks. Use at your own risk.

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Algo: TMA2

Warren Harding | August 2019

Hi,

Here's another low capacity algo for you. You'll need to find more suitable stocks to trade a larger account. I do not recommend trading stock 'Y' with it as it is likely that a number of people are going to try that where I have published this script. That will likely ruin gains for that stock. I have a screener that can find suitable stocks.

This a mean reversion variant. I take the triangular moving average of the candle averages. Each of these candle averages is calculated by summing the open, high, low, and close and dividing by four. To calculate the distance from the tma to set the buy and sell points, I use half of the full range of the rolling window and subtract or add accordingly.

I have componentized the main calculations in the TMA2 class, so you can quickly copy and paste this component into more complex algos.

Use at your own risk.

Regards,
Warren Harding

Author

Warren Harding

August 2019

Upvote

3 people upvoted this

Shile Wen INVESTOR

UW CFRM | January 2021

Hi Zach,

The algorithm has many technical and stylistic issues, and since it seemed to be a popular algorithm, we've rewrote it to use the SymbolData pattern, replaced the History calls with RollingWindows, and fixed many stylistic issues. Please view the updated algorithm in the attached backtest.

Best,
Shile Wen

Upvote

3 people upvoted this

Zach Oakes INVESTOR

January 2021

Thanks ! It's very cool. It's like an MR take on Trend -- brilliant interpretation, and MUCH nicer than my translation.

Upvote

Mohamed Ajmal INVESTOR

June 2021

How can we implement stoploss / reduce drawdown in this algorithm?

Upvote

Varad Kabade INVESTOR

July 2021

Hi Mohamed Ajmal,
In the last backtest attached by Shile he has implemented the stop-loss which is triggered every day after 10 minutes of market open:

 self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 10), self.trail_stop)

Refer to the following code snippet.

def trail_stop(self):
        for symbol, sd in self.symbol_data.items():
            if not sd.IsReady:
                continue
            mean_price = np.mean(list(sd.close_data))
            # Stop loss percentage is the return over the lookback period
            stoploss = abs(sd.weight * self.lookback / 252.0) + 1    # percent change per period
            if sd.weight > 0 and sd.stopprice is not None:
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price / stoploss
                else:
                    sd.stopprice = max(mean_price / stoploss, sd.stopprice)
                    if mean_price < sd.stopprice:
                        sd.weight = 0
                        self.Liquidate(symbol)            elif sd.weight < 0 and sd.stopprice is not None: 
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price * stoploss
                else:
                    sd.stopprice = min(mean_price * stoploss, sd.stopprice)
                    if mean_price > sd.stopprice:
                       sd.weight = 0
                       self.Liquidate(symbol)            else:
                sd.stopprice = None
+ Expand
- Collapse

Best,
Varad Kabade

Upvote

1 person upvoted this

Andres Arizpe INVESTOR

January 2022

This appears to be a very interesting algo.

Is there any documentation I might use to get a basic understanding of it?

Cheers,

Andres

Upvote

Varad Kabade INVESTOR

January 2022

Hi Andres Arizpe,
The above algorithm consists of components like the scheduled events, rolling window, and standard python libraries like numpy and pandas. We recommend going through the following docs[1, 2], and regarding the libraries, please look for their homepage/documentation. Please feel free to ask any specific doubts about the above algorithm.
Best,
Varad Kabade

Upvote

Shile Wen INVESTOR

UW CFRM | January 2021

Hi Zach,

Best,
Shile Wen

Upvote

3 people upvoted this

Zach Oakes INVESTOR

January 2021

Thanks ! It's very cool. It's like an MR take on Trend -- brilliant interpretation, and MUCH nicer than my translation.

Upvote

Mohamed Ajmal INVESTOR

June 2021

How can we implement stoploss / reduce drawdown in this algorithm?

Upvote

Varad Kabade INVESTOR

July 2021

Hi Mohamed Ajmal,
In the last backtest attached by Shile he has implemented the stop-loss which is triggered every day after 10 minutes of market open:

 self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 10), self.trail_stop)

Refer to the following code snippet.

def trail_stop(self):
        for symbol, sd in self.symbol_data.items():
            if not sd.IsReady:
                continue
            mean_price = np.mean(list(sd.close_data))
            # Stop loss percentage is the return over the lookback period
            stoploss = abs(sd.weight * self.lookback / 252.0) + 1    # percent change per period
            if sd.weight > 0 and sd.stopprice is not None:
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price / stoploss
                else:
                    sd.stopprice = max(mean_price / stoploss, sd.stopprice)
                    if mean_price < sd.stopprice:
                        sd.weight = 0
                        self.Liquidate(symbol)            elif sd.weight < 0 and sd.stopprice is not None: 
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price * stoploss
                else:
                    sd.stopprice = min(mean_price * stoploss, sd.stopprice)
                    if mean_price > sd.stopprice:
                       sd.weight = 0
                       self.Liquidate(symbol)            else:
                sd.stopprice = None
+ Expand
- Collapse

Best,
Varad Kabade

Upvote

1 person upvoted this

Andres Arizpe INVESTOR

January 2022

This appears to be a very interesting algo.

Is there any documentation I might use to get a basic understanding of it?

Cheers,

Andres

Upvote

Varad Kabade INVESTOR

January 2022

Upvote

Shile Wen INVESTOR

UW CFRM | January 2021

Hi Zach,

Best,
Shile Wen

Upvote

3 people upvoted this

Zach Oakes INVESTOR

January 2021

Thanks ! It's very cool. It's like an MR take on Trend -- brilliant interpretation, and MUCH nicer than my translation.

Upvote

Mohamed Ajmal INVESTOR

June 2021

How can we implement stoploss / reduce drawdown in this algorithm?

Upvote

Varad Kabade INVESTOR

July 2021

Hi Mohamed Ajmal,
In the last backtest attached by Shile he has implemented the stop-loss which is triggered every day after 10 minutes of market open:

 self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 10), self.trail_stop)

Refer to the following code snippet.

def trail_stop(self):
        for symbol, sd in self.symbol_data.items():
            if not sd.IsReady:
                continue
            mean_price = np.mean(list(sd.close_data))
            # Stop loss percentage is the return over the lookback period
            stoploss = abs(sd.weight * self.lookback / 252.0) + 1    # percent change per period
            if sd.weight > 0 and sd.stopprice is not None:
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price / stoploss
                else:
                    sd.stopprice = max(mean_price / stoploss, sd.stopprice)
                    if mean_price < sd.stopprice:
                        sd.weight = 0
                        self.Liquidate(symbol)            elif sd.weight < 0 and sd.stopprice is not None: 
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price * stoploss
                else:
                    sd.stopprice = min(mean_price * stoploss, sd.stopprice)
                    if mean_price > sd.stopprice:
                       sd.weight = 0
                       self.Liquidate(symbol)            else:
                sd.stopprice = None
+ Expand
- Collapse

Best,
Varad Kabade

Upvote

1 person upvoted this

Andres Arizpe INVESTOR

January 2022

This appears to be a very interesting algo.

Is there any documentation I might use to get a basic understanding of it?

Cheers,

Andres

Upvote

Varad Kabade INVESTOR

January 2022

Upvote

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