Lookahead analysis¶
This page explains how to validate your strategy in terms of lookahead bias.
Lookahead bias is the bane of any strategy since it is sometimes very easy to introduce this bias, but can be very hard to detect.
Backtesting initializes all timestamps (loads the whole dataframe into memory) and calculates all indicators at once. This means that if your indicators or entry/exit signals look into future candles, this will falsify your backtest.
The lookahead-analysis command requires historic data to be available.
To learn how to get data for the pairs and exchange you're interested in,
head over to the Data Downloading section of the documentation.
lookahead-analysis also supports freqai strategies.
This command internally chains backtests and pokes at the strategy to provoke it to show lookahead bias. This is done by not looking at the strategy code itself, but at changed indicator values and moved entries/exits compared to the full backtest.
lookahead-analysis can use the typical options of Backtesting, but forces the following options:
--cacheis forced to "none".--max-open-tradesis forced to be at least equal to the number of pairs.--dry-run-walletis forced to be basically infinite (1 billion).--stake-amountis forced to be a static 10000 (10k).--enable-protectionsis forced to be off.
These are set to avoid users accidentally generating false positives.
Lookahead-analysis command reference¶
usage: freqtrade lookahead-analysis [-h] [-v] [--no-color] [--logfile FILE]
[-V] [-c PATH] [-d PATH] [--userdir PATH]
[-s NAME] [--strategy-path PATH]
[--recursive-strategy-search]
[--freqaimodel NAME]
[--freqaimodel-path PATH] [-i TIMEFRAME]
[--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,feather,parquet}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT]
[--fee FLOAT] [-p PAIRS [PAIRS ...]]
[--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--timeframe-detail TIMEFRAME_DETAIL]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export {none,trades,signals}]
[--export-filename PATH]
[--freqai-backtest-live-models]
[--minimum-trade-amount INT]
[--targeted-trade-amount INT]
[--lookahead-analysis-exportfilename LOOKAHEAD_ANALYSIS_EXPORTFILENAME]
options:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `feather`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--enable-protections, --enableprotections
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
--timeframe-detail TIMEFRAME_DETAIL
Specify detail timeframe for backtesting (`1m`, `5m`,
`30m`, `1h`, `1d`).
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a space-separated list of strategies to
backtest. Please note that timeframe needs to be set
either in config or via command line. When using this
together with `--export trades`, the strategy-name is
injected into the filename (so `backtest-data.json`
becomes `backtest-data-SampleStrategy.json`
--export {none,trades,signals}
Export backtest results (default: trades).
--export-filename PATH, --backtest-filename PATH
Use this filename for backtest results.Requires
`--export` to be set as well. Example: `--export-filen
ame=user_data/backtest_results/backtest_today.json`
--freqai-backtest-live-models
Run backtest with ready models.
--minimum-trade-amount INT
Minimum trade amount for lookahead-analysis
--targeted-trade-amount INT
Targeted trade amount for lookahead analysis
--lookahead-analysis-exportfilename LOOKAHEAD_ANALYSIS_EXPORTFILENAME
Use this csv-filename to store lookahead-analysis-
results
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--logfile FILE, --log-file FILE
Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH, --data-dir PATH
Path to the base directory of the exchange with
historical backtesting data. To see futures data, use
trading-mode additionally.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
--recursive-strategy-search
Recursively search for a strategy in the strategies
folder.
--freqaimodel NAME Specify a custom freqaimodels.
--freqaimodel-path PATH
Specify additional lookup path for freqaimodels.
Note
The above output was reduced to options that lookahead-analysis adds on top of regular backtesting commands.
Introduction¶
Many strategies, without the programmer knowing, have fallen prey to lookahead bias. This typically makes the strategy backtest look profitable, sometimes to extremes, but this is not realistic as the strategy is "cheating" by looking at data it would not have in dry or live modes.
The reason why strategies can "cheat" is because the freqtrade backtesting process populates the full dataframe including all candle timestamps at the outset. If the programmer is not careful or oblivious how things work internally (which sometimes can be really hard to find out) then the strategy will look into the future.
This command is made to try to verify the validity in the form of the aforementioned lookahead bias.
How does the command work?¶
It will start with a backtest of all pairs to generate a baseline for indicators and entries/exits.
After this initial backtest runs, it will look if the minimum-trade-amount is met and if not cancel the lookahead-analysis for this strategy.
If this happens, use a wider timerange to get more trades for the analysis, or use a timerange where more trades occur.
After setting the baseline it will then do additional backtest runs for every entry and exit separately.
When these verification backtests complete, it will compare the indicators at the signal candles (both entry or exit)
and report the bias.
After all signals have been verified or falsified a result table will be generated for the user to see.
How to find and remove bias? How can I salvage a biased strategy?¶
If you found a biased strategy online and want to have the same results, just without bias, then you will be out of luck most of the time. Usually the bias in the strategy is THE driving factor for "too good to be true" profits. Removing conditions or indicators that push the profits up from bias will usually make the strategy significantly worse. You might be able to salvage it partially if the biased indicators or conditions are not the core of the strategy, or there are other entry and exit signals that are not biased.
Examples of lookahead-bias¶
shift(-10)looks 10 candles into the future.- Using
iloc[]in populate_* functions to access a specific row in the dataframe. - For-loops are prone to introduce lookahead bias if you don't tightly control which numbers are looped through.
- Aggregation functions like
.mean(),.min()and.max(), without a rolling window, will calculate the value over the whole dataframe, so the signal candle will "see" a value including future candles. A non-biased example would be to look back candles usingrolling()instead: e.g.dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean() ta.MACD(dataframe, 12, 26, 1)will introduce bias with a signalperiod of 1.
What do the columns in the results table mean?¶
filename: name of the checked strategy filestrategy: checked strategy class namehas_bias: result of the lookahead-analysis.Nowould be good,Yeswould be bad.total_signals: number of checked signals (default is 20)biased_entry_signals: found bias in that many entriesbiased_exit_signals: found bias in that many exitsbiased_indicators: shows you the indicators themselves that are defined in populate_indicators
You might get false positives in the biased_exit_signals if you have biased entry signals paired with those exits.
However, a biased entry will usually result in a biased exit too,
even if the exit itself does not produce the bias -
especially if your entry and exit conditions use the same biased indicator.
Address the bias in the entries first, then address the exits.
Caveats¶
lookahead-analysiscan only verify / falsify the trades it calculated and verified. If the strategy has many different signals / signal types, it's up to you to select appropriate parameters to ensure that all signals have triggered at least once. Signals that are not triggered will not have been verified.
This would lead to a false-negative, i.e. the strategy will be reported as non-biased.lookahead-analysishas access to the same backtesting options and this can introduce problems. Please don't use any options like enabling position stacking as this will distort the number of checked signals. If you decide to do so, then make doubly sure that you won't ever run out ofmax_open_tradesslots, and that you have enough capital in the backtest wallet configuration.- In the results table, the
biased_indicatorscolumn will falsely flag FreqAI target indicators defined inset_freqai_targets()as biased.
These are not biased and can safely be ignored.