u/Academic_Taste8710

Developed a trend-following algo (long only, higher timeframes H2-H4) that's already showing solid results on BTC-USD over the long run, but I have doubts about some functions/indicators - would really appreciate feedback from those in the know :)

Brief overview:

0. The algorithm's goal is to safely capture large trending moves in the traded asset. Returns - multiples above simple spot buying, risk - significantly lower than the asset's peak drawdowns. Designed for scaling capital over time and diversifying across low-correlation assets. Profitable runs don't happen often - the goal is not to miss them and to extract maximum profit.

1. Entry pattern is simple - price on the working timeframe closes above a specific MA + filter conditions are met = opens long at the next candle open.

2. Pyramiding along the trend -adding positions with fixed % risk - entry logic stays the same - to maximize profit. Max positions - 20 (but depends on the specific asset chosen).

3. Fixed % stop-loss, take-profit, moving stop-loss to breakeven, dynamic risk per trade in % - individual for each position (from 0.2% to 1%).

4. Exits - long holding periods and slow exits (using Chandelier Exit as it adapts to ATR + additional confirmation) - if price closes below it, by default 1 position is closed. The goal is not to exit too early and capture the trending move as fully as possible.

5. During low-volatility or choppy markets, additional protection comes from drawdown compression on account balance and stop-loss drawdown compression (using statistical patterns to go defensive when the market is awful, and restore risk when trend signs appear).

Questions and areas I'd like to improve:

1. Filtering entries during chop/ranging markets. Anyone have recommendations for good chop/low-volatility filters with reasonable lag that: filter out chop effectively, allow reasonably early trend detection + can be adapted to different trending assets. Timeframe H2-H4.

2. Filtering pyramiding entries. During position scaling, filters are also needed - the logic being that price shows signs of consolidation and trend continuation (typically looks like rally-consolidation-rally-consolidation...) and the goal is to reduce the number of entries during an already ongoing trending move.

Developed a trend-following algo (long only, higher timeframes H2-H4) that's already showing solid results on BTC-USD over the long run, but I have doubts about some functions/indicators - would really appreciate feedback from those in the know :)

Brief overview:

0. The algorithm's goal is to safely capture large trending moves in the traded asset. Returns - multiples above simple spot buying, risk - significantly lower than the asset's peak drawdowns. Designed for scaling capital over time and diversifying across low-correlation assets. Profitable runs don't happen often - the goal is not to miss them and to extract maximum profit.

1. Entry pattern is simple - price on the working timeframe closes above a specific MA + filter conditions are met = opens long at the next candle open.

2. Pyramiding along the trend -adding positions with fixed % risk — entry logic stays the same - to maximize profit. Max positions - 20 (but depends on the specific asset chosen).

3. Fixed % stop-loss, take-profit, moving stop-loss to breakeven, dynamic risk per trade in % - individual for each position (from 0.2% to 1%).

4. Exits - long holding periods and slow exits (using Chandelier Exit as it adapts to ATR + additional confirmation) - if price closes below it, by default 1 position is closed. The goal is not to exit too early and capture the trending move as fully as possible.

5. During low-volatility or choppy markets, additional protection comes from drawdown compression on account balance and stop-loss drawdown compression (using statistical patterns to go defensive when the market is awful, and restore risk when trend signs appear).

Questions and areas I'd like to improve:

1. Filtering entries during chop/ranging markets. Anyone have recommendations for good chop/low-volatility filters with reasonable lag that: filter out chop effectively, allow reasonably early trend detection + can be adapted to different trending assets. Timeframe H2-H4.

2. Filtering pyramiding entries. During position scaling, filters are also needed - the logic being that price shows signs of consolidation and trend continuation (typically looks like rally-consolidation-rally-consolidation...) and the goal is to reduce the number of entries during an already ongoing trending move.

Developed a trend-following algo (long only, higher timeframes H2-H4) that's already showing solid results on BTC-USD over the long run, but I have doubts about some functions/indicators - would really appreciate feedback from those in the know :)

Brief overview:

0. The algorithm's goal is to safely capture large trending moves in the traded asset. Returns - multiples above simple spot buying, risk - significantly lower than the asset's peak drawdowns. Designed for scaling capital over time and diversifying across low-correlation assets. Profitable runs don't happen often - the goal is not to miss them and to extract maximum profit.

1. Entry pattern is simple - price on the working timeframe closes above a specific MA + filter conditions are met = opens long at the next candle open.

2. Pyramiding along the trend -adding positions with fixed % risk — entry logic stays the same - to maximize profit. Max positions - 20 (but depends on the specific asset chosen).

3. Fixed % stop-loss, take-profit, moving stop-loss to breakeven, dynamic risk per trade in % - individual for each position (from 0.2% to 1%).

4. Exits - long holding periods and slow exits (using Chandelier Exit as it adapts to ATR + additional confirmation) - if price closes below it, by default 1 position is closed. The goal is not to exit too early and capture the trending move as fully as possible.

5. During low-volatility or choppy markets, additional protection comes from drawdown compression on account balance and stop-loss drawdown compression (using statistical patterns to go defensive when the market is awful, and restore risk when trend signs appear).

Questions and areas I'd like to improve:

1. Filtering entries during chop/ranging markets. Anyone have recommendations for good chop/low-volatility filters with reasonable lag that: filter out chop effectively, allow reasonably early trend detection + can be adapted to different trending assets. Timeframe H2-H4.

2. Filtering pyramiding entries. During position scaling, filters are also needed - the logic being that price shows signs of consolidation and trend continuation (typically looks like rally-consolidation-rally-consolidation...) and the goal is to reduce the number of entries during an already ongoing trending move.

If you know your stuff - comment or DM, happy to share insights, trying to build a top-tier algo for serious profits :)

I have been in trading for over 15 years, focusing on capital raising and management. I have worked with various trading systems but have concluded that trend algorithms are the most resilient and sustainable over the long term. This is exactly what is needed to not only generate profits but also preserve capital during turbulent periods.

Currently, I am implementing a trend system (a portfolio of algorithms) built on the following principles:

1. A modular algorithm that includes:

- Core blocks: signal structure, signal type, asset and timeframe selection, risk management, and position exit.

- Filtering: several filter levels, trading direction, trading day filters, etc.

- Protection: multi-level drawdown compression.

- Statistical analysis: stop-loss drawdown compression (contraction and expansion based on statistical patterns).

- Additional features: various exit methods, position pyramiding, additional entry conditions, etc.

2. A multi-stage selection process for algorithms based on backtesting and forward testing results over the long term, following the module hierarchy – starting with basic parameters, selecting the best algorithms, and refining them adequately (avoiding overfitting).

3. Assembling the final portfolio from the algorithms that pass all stages, considering:

- Algorithm correlation.

- Strict drawdown limits for each algorithm via drawdown compression – so that if one algorithm "breaks," it does not break the system.

- Using different assets and different timeframes.

4. Automating as many stages as possible (especially testing) and using AI to analyze data during testing and in real trading.

If anyone shares these principles or has worked with something similar, I suggest we discuss, share advice, or get directly involved in the development process :)

I have been in trading for over 15 years, focusing on capital raising and management. I have worked with various trading systems but have concluded that trend algorithms are the most resilient and sustainable over the long term. This is exactly what is needed to not only generate profits but also preserve capital during turbulent periods.

Currently, I am implementing a trend system (a portfolio of algorithms) built on the following principles:

1. A modular algorithm that includes:

- Core blocks: signal structure, signal type, asset and timeframe selection, risk management, and position exit.

- Filtering: several filter levels, trading direction, trading day filters, etc.

- Protection: multi-level drawdown compression.

- Statistical analysis: stop-loss drawdown compression (contraction and expansion based on statistical patterns).

- Additional features: various exit methods, position pyramiding, additional entry conditions, etc.

2. A multi-stage selection process for algorithms based on backtesting and forward testing results over the long term, following the module hierarchy – starting with basic parameters, selecting the best algorithms, and refining them adequately (avoiding overfitting).

3. Assembling the final portfolio from the algorithms that pass all stages, considering:

- Algorithm correlation.

- Strict drawdown limits for each algorithm via drawdown compression – so that if one algorithm "breaks," it does not break the system.

- Using different assets and different timeframes.

4. Automating as many stages as possible (especially testing) and using AI to analyze data during testing and in real trading.

If anyone shares these principles or has worked with something similar, I suggest we discuss, share advice, or get directly involved in the development process :)

I have been in trading for over 15 years, focusing on capital raising and management. I have worked with various trading systems but have concluded that trend algorithms are the most resilient and sustainable over the long term. This is exactly what is needed to not only generate profits but also preserve capital during turbulent periods.

Currently, I am implementing a trend system (a portfolio of algorithms) built on the following principles:

1. A modular algorithm that includes:

- Core blocks: signal structure, signal type, asset and timeframe selection, risk management, and position exit.

- Filtering: several filter levels, trading direction, trading day filters, etc.

- Protection: multi-level drawdown compression.

- Statistical analysis: stop-loss drawdown compression (contraction and expansion based on statistical patterns).

- Additional features: various exit methods, position pyramiding, additional entry conditions, etc.

2. A multi-stage selection process for algorithms based on backtesting and forward testing results over the long term, following the module hierarchy – starting with basic parameters, selecting the best algorithms, and refining them adequately (avoiding overfitting).

3. Assembling the final portfolio from the algorithms that pass all stages, considering:

- Algorithm correlation.

- Strict drawdown limits for each algorithm via drawdown compression – so that if one algorithm "breaks," it does not break the system.

- Using different assets and different timeframes.

4. Automating as many stages as possible (especially testing) and using AI to analyze data during testing and in real trading.

If anyone shares these principles or has worked with something similar, I suggest we discuss, share advice, or get directly involved in the development process :)

I have been in trading for over 15 years, focusing on capital raising and management. I have worked with various trading systems but have concluded that trend algorithms are the most resilient and sustainable over the long term. This is exactly what is needed to not only generate profits but also preserve capital during turbulent periods.

Currently, I am implementing a trend system (a portfolio of algorithms) built on the following principles:

1. A modular algorithm that includes:

- Core blocks: signal structure, signal type, asset and timeframe selection, risk management, and position exit.

- Filtering: several filter levels, trading direction, trading day filters, etc.

- Protection: multi-level drawdown compression.

- Statistical analysis: stop-loss drawdown compression (contraction and expansion based on statistical patterns).

- Additional features: various exit methods, position pyramiding, additional entry conditions, etc.

2. A multi-stage selection process for algorithms based on backtesting and forward testing results over the long term, following the module hierarchy – starting with basic parameters, selecting the best algorithms, and refining them adequately (avoiding overfitting).

3. Assembling the final portfolio from the algorithms that pass all stages, considering:

- Algorithm correlation.

- Strict drawdown limits for each algorithm via drawdown compression – so that if one algorithm "breaks," it does not break the system.

- Using different assets and different timeframes.

4. Automating as many stages as possible (especially testing) and using AI to analyze data during testing and in real trading.

If anyone shares these principles or has worked with something similar, I suggest we discuss, share advice, or get directly involved in the development process :)