Freqtrade Revealed: 7-Day Journey in Algorithmic Trading for Crypto Futures Market

Making Profits on Freqtrade open-Source platform using Algorithmic Trading. Revolutionized With Famous RSI, MACD, Bollinger Bands, ADX, EMA

Greetings to all Readers,

I have previously written an article on — The 8787%+ ROI Algo Strategy Unveiled for Crypto Futures! Revolutionized With Famous RSI, MACD, Bollinger Bands, ADX, EMA Link

P.S. — For the first-time readers and new to freqtrade, I request you to check out above given link for understanding Freqtrade, Algo Trading, Backtesting and other important concepts.

The below strategy is a modified version of above mentioned strategy with article link provided. Here, the whole algorithmic trading setup is made in such a way that, the bot enters trade with 1% (10 USDT) per trade of total USDT value. We can do it upto 2.5% (25 USDT for 1000 USDT stake) per trade to get higher returns (comparatively to 1% trade entry). Main target for this strategy is to achieve around 6–20% return per month which corresponds to annually returns of around 72–240% with low staking amount (Below 20000 USDT, as we may face liquidation issue for few cryptocurrency assets).

I have run dry-run in live market for past 7 days to check the performance of the bot, got great winning rate (which outperformed backtested results for the first time) after 450+ successful trades completion.

Below are the details for the same.

7 days daily return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade
7 days daily return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade

If we observe above, I have taken 1% stake per trade risking maximum of 30% (approx.) as Stop Loss and applied trailing stop loss, trailing stop profit, ROI, and other exit strategy to make an average of 2.5-3% per trade profit.

The total Staking amount is set to1000 USDT

Stake amount per trade is set to10 USDT

Leverage used for Futures market on Binance Platform is set to — 5x per trade

Maximum Trades is set to unlimited (but maximum it can take is up to 40, as I set VolumeFilter to top 40 high volume coins in 1 day time span).

7 days Summary return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade
7 days Summary return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade

You can find the whole code here https://patreon.com/pppicasso

We can observe from above general Summary of the bot performance is,

win-rate is at 94.889%

Total trades count 473

Total Wins 427 and Total Loss 23

Max Drawdown during the trading session is at — 1.47%

7 days Performance return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade
7 days Performance return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade
7 days Performance return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade — 2

Bot has given exceptional returns for few of the crypto assets and gave huge losses for few other during the dry-run

Using performanceFilter (more info from freqtrade websiteLink) or (to know how run a successful freqtrade bot to make profits, please go through public article information provided by me on my Patreon platform here — Link ) from freqtrade setup, we can further limit the bot the perform well with those assets, which run well on this particular algorithm.

Usually, every asset will have a certain volatility level and a particular strategy works well when it meets with certain level of market conditions which includes trend (up-trend, down-trend, side ways), volume, volatility and momentum of the assets.

I have not used PerformanceFilter here, rather I manually added 4–5 assets which hit SL more than twice to blacklist (from the GUI).

Blacklist and White List based on Freqtrade Filters Used and few Manually added
Blacklist and White List based on Freqtrade Filters Used and few Manually added

I have manually added everything on blacklist to avoid other trading pairs except for USDT alone. and also to avoid stable coin pairs if any comes under filters I have kept programmatically ( VolumePairList, ageFilter, VolatilityFilter, ShuffleFilter, RangeStabilityFilter more info from freqtrade websiteLink)

7 days Daily return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade
7 days Daily return of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade

If we observe from the above daily returns, Bot has performed exceptional during up-trend (26th November 2023) and during down-trend (29th November 2023) and has given low losses/returns during side way market (remaining days ) , the bot needs further testing to how it reacts during sudden market change (V shape direction). From downtrend to sudden up trend, if that specific market is also confirmed, we can use this bot for live trading (I suggest to run 2 months of dry-run minimum before investing actual money for safety purpose) , I have not used PerformanceFilter here, rather I manually added 4–5 assets which hit SL more than twice to blacklist(from the GUI).

Also, win rate is above 94%, which outperformed backtesting results (for very first time I have observed this) usually after 300+ trades, winning rate equals backtested results by 1–2% error. backtested results shown only 78% (approx.)

Backtest Results of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade
Backtested Results of the RSI_MACD_BB_1h_2 Version-2(2.2.6) on Freqtrade

Freqtrade backtesting summary profit for RSI, MACD, Bollinger Bands Crypto Algorithmic Strategy 2023

You can find the whole code here https://patreon.com/pppicasso

By investing 1000 USDT, (1 USD approximately equal to 1 USDT with 1–2% variance)

for a period of 1024 days from 2021–01–06 00:00:00 up to 2023–10–27 00:00:00 (1024 days)

with maximum open trades at any given point of time being 129

maximum stake in each trade entry being around 10 USDT,

has given a Profit of 5562% Profit return on investment (ROI).

The Absolute Draw-down mentioned from results is at — 1.34%

Daily Win to Lose Ratio is at 775 days of WIN, 254 Days loss and 0 Days of Draw (Open trades which haven’t closed yet)

Average Daily profit is at5.42% per day

Daily Average Trades is 321.89 approximate

Market Returns Have been (if you buy and hold Bitcoin (BTCUSDT) for the above mentioned period the returns are mentioned here, instead of trading) — 37.70%

Time Frame used is 1h

Conclusion

The exploration of algorithmic trading on Freqtrade, particularly with the modified RSI_MACD_BB_1h_2 Version-2(2.2.6) strategy, has yielded promising results, offering a glimpse into the potential of leveraging automated systems in the cryptocurrency futures market. Over a seven-day dry run, the bot demonstrated a high win rate of 94.889% across 473 trades, outperforming initial backtesting results and indicating robustness in various market conditions, including uptrends, downtrends, and sideways movements.

The strategy’s risk management approach, incorporating a maximum stake of 1% per trade and a comprehensive exit strategy involving trailing stops and ROI-based exits, contributed to an overall low drawdown of 1.47%. This careful balance of risk and reward underscores the importance of a methodical approach in algorithmic trading.

Further refinement of the strategy, such as using Freqtrade’s `PerformanceFilter` or manual adjustments like blacklisting under-performing assets, demonstrates the adaptability and customization potential of algorithmic trading. These adjustments help tailor the bot’s performance to specific market behaviors and asset characteristics, enhancing its efficacy.

The comparison of the bot’s performance with traditional buy-and-hold strategies — a staggering 5562% ROI over 1024 days compared to Bitcoin’s 37.70% return in the same period — highlights the significant advantage of algorithmic trading in maximizing returns and capitalizing on market movements.

While the results are encouraging, they also emphasize the need for continued testing, particularly to understand the bot’s response to sudden market shifts. A minimum two-month dry-run period is advisable before transitioning to live trading to ensure safety and reliability.

In conclusion, the integration of advanced technical indicators and meticulous strategy design in Freqtrade has shown not only the capability to generate substantial returns but also the potential for broader applications in the field of cryptocurrency trading. As the market continues to evolve, the blend of technology, strategy, and risk management will remain crucial for success in algorithmic trading.

For those interested in exploring this strategy further or adopting similar approaches, the complete code and additional insights are available at Patreon link.

Thank you, Readers.

I hope you have found this article on Algorithmic strategy to be informative and helpful. As a creator, I am dedicated to providing valuable insights and analysis on cryptocurrency, stock market and other assets management.

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Regards,

Puranam Pradeep Picasso

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Puranam Pradeep Picasso - ImbueDesk Profile

Algorithmic Trader, AI/ML & Crypto Enthusiast, Certified Blockchain Architect, Certified Lean Six SIgma Green Belt, Certified SCRUM Master and Entrepreneur