Freqtrade Review: What GitHub and Reddit Users Are Saying About the Automated Trading Bot

Freqtrade is perhaps the largest open source AI automated trading software on GitHub, with more than 54,000 stars and 11,000 forks at the time of this review. Written in Python, it can connect to major cryptocurrency exchanges, execute spot and futures strategies, backtest historical data, optimize strategy parameters, run machine learning models through

FreqAI, and manage a live bot through a web interface or Telegram.

The repository is mature enough that the conversation around it has moved beyond whether the software works at all. GitHub issues and Reddit discussions increasingly focus on a harder question: what happens after somebody has a functioning automated trading platform and still needs to find a strategy worth running?

The answer from users is considerably less glamorous than the typical trading bot advertisement. Freqtrade appears to have earned respect as infrastructure, while profitability remains firmly outside the package.

Users seem to like Freqtrade for the boring parts

One of the clearest descriptions appeared in a June 2026 Reddit discussion from a developer with a Python and TensorFlow background. A commenter argued that Freqtrade was useful precisely because it already handled exchange connections, order sizing, backtesting, configuration, protections, and logging, allowing the trader to keep research and machine learning work separate from the execution system.

That characterization appears repeatedly across discussions about automated trading. Building a strategy can be intellectually appealing, but operating it requires a considerable amount of machinery around the strategy itself. Orders can partially fill, connections fail, exchanges return unexpected values, APIs change, positions need to be reconciled, and a running system has to preserve enough information to explain what it did hours later.

Freqtrade has spent years accumulating solutions to those operational problems. Its official documentation includes dry trading, historical backtesting, exchange integration, position management, strategy optimization, FreqAI, plotting, a web interface, and several analysis tools designed to detect problems before money is placed behind a strategy.

This is where the project seems to receive its strongest praise. Users are not generally describing Freqtrade as an algorithm that discovers profitable trades for them. They are describing it as the machinery that lets their own algorithm reach an exchange without requiring them to rebuild an entire trading platform first.

Public Freqtrade strategies receive a much colder reception

The community becomes markedly more skeptical when the discussion moves from Freqtrade itself to strategies published for it.

A Reddit post from August 2026 reported testing 42 public Freqtrade strategies across eight years of Bitcoin data and found that 33 lost money under that particular test configuration. A commenter immediately challenged part of the methodology, noting that many strategies in the public repository were designed to trade broader collections of assets and could behave very differently when reduced to Bitcoin alone.

That exchange captures the larger problem with judging trading systems from public backtests. A strategy can fail because its original premise was poor, but it can also fail because the market, asset universe, position limits, fees, timeframe, or test procedure differs from the environment for which it was constructed.

Freqtrade's own public strategy repository says essentially the same thing. Its documentation warns that performance depends heavily on the selected pairs, timeframe, and historical period, and advises users to run their own backtests rather than assuming published results will carry into another configuration.

A larger independent experiment posted to Reddit in August went further. The author reported retesting 571 public Freqtrade strategy classes and found that 55 survived the statistical filters used in the study, yet none exceeded the return from simply holding the same underlying assets during the comparison period. The same analysis reported much smaller drawdowns for many of those strategies, suggesting that raw return was not the only characteristic changing.

Those numbers come from community research rather than an independent academic replication, so they should not be treated as a universal measurement of every Freqtrade strategy. They still illustrate how differently the community discusses the platform and the strategies running inside it.

Reddit does not trust beautiful backtests very easily

A separate Freqtrade user posted a backtest in April 2026 claiming a 281 percent return with only 0.84 percent maximum drawdown over roughly three years. The first substantive response was skepticism about whether such a smooth historical result would survive slippage, fees, changing market conditions, and actual execution. The strategy author agreed with the criticism and acknowledged that there had not yet been a meaningful live test.

That reaction is representative of the more technically experienced algorithmic trading community. Historical profitability attracts attention, but an unusually clean equity curve often produces additional questions rather than immediate admiration.

Recent discussions repeatedly return to the difference between discovering a pattern in historical data and maintaining that behavior after deployment. One 2026 discussion described algorithmic profitability as a multiyear engineering problem rather than a quick side project, while other commenters emphasized execution failures, slippage, rate limits, partial fills, and differences between historical and live market data.

Freqtrade cannot remove those problems. What it can do is provide enough tooling to expose some of them before a trader commits substantial capital.

GitHub issues show what running a real bot actually involves

The Freqtrade issue tracker contains a useful counterweight to screenshots of profitable backtests. Current reports concern exchange behavior, FreqAI prediction state, leverage information, network failures, database connections, and differences between historical and live execution.

One recent issue involved a live Kraken bot on Freqtrade 2026.8 crashing after an entry order remained open and the exchange returned an unexpected fee value. The user reported that the process terminated before the trade was persisted correctly. A Freqtrade maintainer responded that the behavior appeared to originate in CCXT after a Kraken API change and noted that the upstream problem had already been corrected, while also pointing out that the user's installed CCXT version did not match the version pinned by Freqtrade.

Another issue reported that restarting a FreqAI bot could leave individual trading pairs unable to process new candles after duplicate prediction timestamps entered the stored prediction history. The August 2026 release notes show several repairs around historical prediction recovery, duplicate candles, prediction overlap, and behavior following downtime.

These are not evidence that Freqtrade is unusually unreliable. A project that connects continuously to cryptocurrency exchanges, maintains databases, trains models, processes live market data, and places real orders has a much wider failure surface than a backtesting library running against static files.

The activity around those reports also shows that the project remains actively maintained. Version 2026.8 was released on August 31 with changes across FreqAI, exchange handling, pair selection, historical data processing, dependency updates, and strategy behavior.

Backtesting can still be misunderstood

One particularly revealing GitHub discussion concerned Freqtrade's order book data during historical backtesting. A user reported that a strategy could request current order book information while replaying old market candles, allowing present market conditions to influence a historical test.

The maintainer did not treat this as an undiscovered defect. Freqtrade already documents that order book information is not part of the historical dataset and that using the relevant method during backtesting will return current values, producing an invalid historical comparison.

This exposes one of Freqtrade's less comfortable strengths. The software gives strategy developers considerable freedom, including enough freedom to construct a backtest that appears meaningful while quietly combining incompatible information.

The project provides analysis tools and documentation intended to catch several forms of this problem, but software cannot completely protect a trader from a flawed research design. Reddit's growing frustration with strategies containing lookahead bias reflects the same concern. A highly upvoted August discussion complained that trading forums had become crowded with automated strategies whose creators did not understand the bias embedded in their own tests.

The documentation gets mixed reactions

Freqtrade's documentation is extensive, which does not necessarily make it easy to absorb. A Reddit user searching for a self hosted trading platform described Freqtrade as providing most of the desired functionality while also describing the documentation as difficult to navigate.

The project itself is unusually explicit about the expected skill level. Its README recommends beginning in dry trading mode and says users should have Python knowledge before committing real money.

Those two observations are compatible. Freqtrade has enough functionality that documentation becomes part of the product rather than a short installation guide. Learning the framework means understanding exchange configuration, historical data, strategy callbacks, position management, execution behavior, optimization, and the boundaries between backtesting and live information.

Someone looking for a button labeled Start Profitable Bot is likely to encounter friction. Someone who already understands Python and wants an existing execution framework may interpret the same complexity very differently.

Freqtrade versus building your own bot

Reddit discussions about Freqtrade frequently divide users into two camps. One group prefers building trading infrastructure directly against broker or exchange APIs because complete control can become valuable for specialized execution. The other group sees little reason to reproduce years of work on order handling, persistence, exchange compatibility, logging, backtesting, and monitoring before testing the actual strategy.

A December 2024 discussion comparing Freqtrade with Hummingbot followed this pattern. One participant preferred Freqtrade's interface, while another argued that specialized medium or high frequency execution could justify writing a dedicated client rather than relying on a general framework.

Freqtrade occupies the broad middle. It is considerably more complete than a collection of exchange API scripts, but it is not designed to compete with specialized institutional infrastructure where latency and direct market access dominate the architecture.

For many individual developers, that compromise appears to be exactly why the project remains popular.

What people are really saying about Freqtrade

The community conversation around Freqtrade is more favorable than the conversation around automated trading itself.

Users routinely recommend the framework for execution, testing, exchange integration, logging, and operational infrastructure. The skepticism appears when somebody assumes those capabilities are equivalent to a profitable strategy.

Public strategies often perform poorly outside the conditions for which they were designed. Attractive historical results receive aggressive scrutiny. Machine learning adds another research layer without eliminating overfitting, execution drift, or changing market structure. Live trading introduces failures that historical simulation cannot reproduce perfectly.

Freqtrade does not solve those problems, and its community does not appear to expect it to.

Its value is more concrete. It provides a mature laboratory and execution system in which those problems can be investigated without requiring every trader to build the laboratory first.

For an automated trading project, that may be a more credible endorsement than another screenshot showing a perfect equity curve.

Freqtrade on GitHub : https://github.com/freqtrade/freqtrade