lychiyu/freqtrade_origin
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2024-11-14 12:13:57 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Merge pull request #10779 from froggleston/frog-strat101-docs-1

Browse Source 
Improve strategy development documentation
This commit is contained in:
Matthias 2024-10-21 06:41:59 +02:00 committed by GitHub
commit 4f0380bb2c
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 426 additions and 126 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

196
docs/strategy-101.md Normal file
View File

@ -0,0 +1,196 @@
# Freqtrade Strategies 101: A Quick Start for Strategy Development
For the purposes of this quick start, we are assuming you are familiar with the basics of trading, and have read the
[Freqtrade basics](bot-basics.md) page.
## Required Knowledge
A strategy in Freqtrade is a Python class that defines the logic for buying and selling cryptocurrency `assets`.
Assets are defined as `pairs`, which represent the `coin` and the `stake`. The coin is the asset you are trading using another currency as the stake.
Data is supplied by the exchange in the form of `candles`, which are made up of a six values: `date`, `open`, `high`, `low`, `close` and `volume`.
`Technical analysis` functions analyse the candle data using various computational and statistical formulae, and produce secondary values called `indicators`.
Indicators are analysed on the asset pair candles to generate `signals`.
Signals are turned into `orders` on a cryptocurrency `exchange`, i.e. `trades`.
We use the terms `entry` and `exit` instead of `buying` and `selling` because Freqtrade supports both `long` and `short` trades.
- **long**: You buy the coin based on a stake, e.g. buying the coin BTC using USDT as your stake, and you make a profit by selling the coin at a higher rate than you paid for. In long trades, profits are made by the coin value going up versus the stake.
- **short**: You borrow capital from the exchange in the form of the coin, and you pay back the stake value of the coin later. In short trades profits are made by the coin value going down versus the stake (you pay the loan off at a lower rate).
Whilst Freqtrade supports spot and futures markets for certain exchanges, for simplicity we will focus on spot (long) trades only.
## Structure of a Basic Strategy
### Main dataframe
Freqtrade strategies use a tabular data structure with rows and columns known as a `dataframe` to generate signals to enter and exit trades.
Each pair in your configured pairlist has its own dataframe. Dataframes are indexed by the `date` column, e.g. `2024-06-31 12:00`.
The next 5 columns represent the `open`, `high`, `low`, `close` and `volume` (OHLCV) data.
### Populate indicator values
The `populate_indicators` function adds columns to the dataframe that represent the technical analysis indicator values.
Examples of common indicators include Relative Strength Index, Bollinger Bands, Money Flow Index, Moving Average, and Average True Range.
Columns are added to the dataframe by calling technical analysis functions, e.g. ta-lib's RSI function `ta.RSI()`, and assigning them to a column name, e.g. `rsi`
```python
dataframe['rsi'] = ta.RSI(dataframe)
```
??? Hint "Technical Analysis libraries"
Different libraries work in different ways to generate indicator values. Please check the documentation of each library to understand
how to integrate it into your strategy. You can also check the [Freqtrade example strategies](https://github.com/freqtrade/freqtrade-strategies) to give you ideas.
### Populate entry signals
The `populate_entry_trend` function defines conditions for an entry signal.
The dataframe column `enter_long` is added to the dataframe, and when a value of `1` is in this column, Freqtrade sees an entry signal.
??? Hint "Shorting"
To enter short trades, use the `enter_short` column.
### Populate exit signals
The `populate_exit_trend` function defines conditions for an exit signal.
The dataframe column `exit_long` is added to the dataframe, and when a value of `1` is in this column, Freqtrade sees an exit signal.
??? Hint "Shorting"
To exit short trades, use the `exit_short` column.
## A simple strategy
Here is a minimal example of a Freqtrade strategy:
```python
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta
class MyStrategy(IStrategy):
# set the initial stoploss to -10%
stoploss = -0.10
# exit profitable positions at any time when the profit is greater than 1%
minimal_roi = {"0": 0.01}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# generate values for technical analysis indicators
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# generate entry signals based on indicator values
dataframe.loc[
(dataframe['rsi'] < 30),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# generate exit signals based on indicator values
dataframe.loc[
(dataframe['rsi'] > 70),
'exit_long'] = 1
return dataframe
```
## Making trades
When a signal is found (a `1` in an entry or exit column), Freqtrade will attempt to make an order, i.e. a `trade` or `position`.
Each new trade position takes up a `slot`. Slots represent the maximum number of concurrent new trades that can be opened.
The number of slots is defined by the `max_open_trades` [configuration](configuration.md) option.
However, there can be a range of scenarios where generating a signal does not always create a trade order. These include:
- not enough remaining stake to buy an asset, or funds in your wallet to sell an asset (including any fees)
- not enough remaining free slots for a new trade to be opened (the number of positions you have open equals the `max_open_trades` option)
- there is already an open trade for a pair (Freqtrade cannot stack positions - however it can [adjust existing positions](strategy-callbacks.md#adjust-trade-position))
- if an entry and exit signal is present on the same candle, they are considered as [colliding](strategy-customization.md#colliding-signals), and no order will be raised
- the strategy actively rejects the trade order due to logic you specify by using one of the relevant [entry](strategy-callbacks.md#trade-entry-buy-order-confirmation) or [exit](strategy-callbacks.md#trade-exit-sell-order-confirmation) callbacks
Read through the [strategy customization](strategy-customization.md) documentation for more details.
## Backtesting and forward testing
Strategy development can be a long and frustrating process, as turning our human "gut instincts" into a working computer-controlled
("algo") strategy is not always straightforward.
Therefore a strategy should be tested to verify that it is going to work as intended.
Freqtrade has two testing modes:
- **backtesting**: using historical data that you [download from an exchange](data-download.md), backtesting is a quick way to assess performance of a strategy. However, it can be very easy to distort results so a strategy will look a lot more profitable than it really is. Check the [backtesting documentation](backtesting.md) for more information.
- **dry run**: often referred to as _forward testing_, dry runs use real time data from the exchange. However, any signals that would result in trades are tracked as normal by Freqtrade, but do not have any trades opened on the exchange itself. Forward testing runs in real time, so whilst it takes longer to get results it is a much more reliable indicator of **potential** performance than backtesting.
Dry runs are enabled by setting `dry_run` to true in your [configuration](configuration.md#using-dry-run-mode).
!!! Warning "Backtests can be very inaccurate"
There are many reasons why backtest results may not match reality. Please check the [backtesting assumptions](backtesting.md#assumptions-made-by-backtesting) and [common strategy mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) documentation.
Some websites that list and rank Freqtrade strategies show impressive backtest results. Do not assume these results are achieveable or realistic.
??? Hint "Useful commands"
Freqtrade includes two useful commands to check for basic flaws in strategies: [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md).
### Assessing backtesting and dry run results
Always dry run your strategy after backtesting it to see if backtesting and dry run results are sufficiently similar.
If there is any significant difference, verify that your entry and exit signals are consistent and appear on the same candles between the two modes. However, there will always be differences between dry runs and backtests:
- Backtesting assumes all orders fill. In dry runs this might not be the case if using limit orders or there is no volume on the exchange.
- Following an entry signal on candle close, backtesting assumes trades enter at the next candle's open price (unless you have custom pricing callbacks in your strategy). In dry runs, there is often a delay between signals and trades opening.
This is because when new candles come in on your main timeframe, e.g. every 5 minutes, it takes time for Freqtrade to analyse all pair dataframes. Therefore, Freqtrade will attempt to open trades a few seconds (ideally a small a delay as possible)
after candle open.
- As entry rates in dry runs might not match backtesting, this means profit calculations will also differ. Therefore, it is normal if ROI, stoploss, trailing stoploss and callback exits are not identical.
- The more computational "lag" you have between new candles coming in and your signals being raised and trades being opened will result in greater price unpredictability. Make sure your computer is powerful enough to process the data for the number
of pairs you have in your pairlist within a reasonable time. Freqtrade will warn you in the logs if there are significant data processing delays.
## Controlling or monitoring a running bot
Once your bot is running in dry or live mode, Freqtrade has five mechanisms to control or monitor a running bot:
- **[FreqUI](freq-ui.md)**: The easiest to get started with, FreqUI is a web interface to see and control current activity of your bot.
- **[Telegram](telegram-usage.md)**: On mobile devices, Telegram integration is available to get alerts about your bot activity and to control certain aspects.
- **[FTUI](https://github.com/freqtrade/ftui)**: FTUI is a terminal (command line) interface to Freqtrade, and allows monitoring of a running bot only.
- **[REST API](rest-api.md)**: The REST API allows programmers to develop their own tools to interact with a Freqtrade bot.
- **[Webhooks](webhook-config.md)**: Freqtrade can send information to other services, e.g. discord, by webhooks.
### Logs
Freqtrade generates extensive debugging logs to help you understand what's happening. Please familiarise yourself with the information and error messages you might see in your bot logs.
## Final Thoughts
Algo trading is difficult, and most public strategies are not good performers due to the time and effort to make a strategy work profitably in multiple scenarios.
Therefore, taking public strategies and using backtests as a way to assess performance is often problematic. However, Freqtrade provides useful ways to help you make decisions and do your due diligence.
There are many different ways to achieve profitability, and there is no one single tip, trick or config option that will fix a poorly performing strategy.
Freqtrade is an open source platform with a large and helpful community - make sure to visit our [discord channel](https://discord.gg/p7nuUNVfP7) to discuss your strategy with others!
As always, only invest what you are willing to lose.
## Conclusion
Developing a strategy in Freqtrade involves defining entry and exit signals based on technical indicators. By following the structure and methods outlined above, you can create and test your own trading strategies.
Common questions and answers are available on our [FAQ](faq.md).
To continue, refer to the more in-depth [Freqtrade strategy customization documentation](strategy-customization.md).

355
docs/strategy-customization.md
View File

@ -2,52 +2,93 @@
This page explains how to customize your strategies, add new indicators and set up trading rules.
Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
If you haven't already, please familiarize yourself with:
- the [Freqtrade strategy 101](freqtrade-101.md), which provides a quick start to strategy development
- the [Freqtrade bot basics](bot-basics.md), which provides overall info on how the bot operates
## Develop your own strategy
The bot includes a default strategy file.
Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
You will however most likely have your own idea for a strategy.
This document intends to help you convert your strategy idea into your own strategy.
To get started, use `freqtrade new-strategy --strategy AwesomeStrategy` (you can obviously use your own naming for your strategy).
This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
This document intends to help you convert your ideas into a working strategy.
### Generating a strategy template
To get started, you can use the command:
```bash
freqtrade new-strategy --strategy AwesomeStrategy
```
This will create a new strategy called `AwesomeStrategy` from a template, which will be located using the filename `user_data/strategies/AwesomeStrategy.py`.
!!! Note
This is just a template file, which will most likely not be profitable out of the box.
There is a difference between the *name* of the strategy and the filename. In most commands, Freqtrade uses the *name* of the strategy, *not the filename*.
!!! Note
The `new-strategy` command generates starting examples which will not be profitable out of the box.
??? Hint "Different template levels"
`freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with most callbacks defined.
`freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with more complicated features defined.
### Anatomy of a strategy
A strategy file contains all the information needed to build a good strategy:
A strategy file contains all the information needed to build the strategy logic:
- Candle data in OHLCV format
- Indicators
- Entry strategy rules
- Exit strategy rules
- Minimal ROI recommended
- Stoploss strongly recommended
- Entry logic
- Signals
- Exit logic
- Signals
- Minimal ROI
- Callbacks ("custom functions")
- Stoploss
- Fixed/absolute
- Trailing
- Callbacks ("custom functions")
- Pricing [optional]
- Position adjustment [optional]
The bot also include a sample strategy called `SampleStrategy` you can update: `user_data/strategies/sample_strategy.py`.
You can test it with the parameter: `--strategy SampleStrategy`
The bot includes a sample strategy called `SampleStrategy` that you can use as a basis: `user_data/strategies/sample_strategy.py`.
You can test it with the parameter: `--strategy SampleStrategy`. Remember that you use the strategy class name, not the filename.
Additionally, there is an attribute called `INTERFACE_VERSION`, which defines the version of the strategy interface the bot should use.
The current version is 3 - which is also the default when it's not set explicitly in the strategy.
Future versions will require this to be set.
You may see older strategies set to interface version 2, and these will need to be updated to v3 terminology as future versions will require this to be set.
Starting the bot in dry or live mode is accomplished using the `trade` command:
```bash
freqtrade trade --strategy AwesomeStrategy
```
### Bot modes
Freqtrade strategies can be processed by the Freqtrade bot in 5 main modes:
- backtesting
- hyperopting
- dry ("forward testing")
- live
- FreqAI (not covered here)
Check the [configuration documentation](configuration.md) about how to set the bot to dry or live mode.
**Always use dry mode when testing as this gives you an idea of how your strategy will work in reality without risking capital.**
## Diving in deeper
**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py)
file as reference.**
!!! Note "Strategies and Backtesting"
To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
To avoid problems and unexpected differences between backtesting and dry/live modes, please be aware
that during backtesting the full time range is passed to the `populate_*()` methods at once.
It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
@ -57,14 +98,22 @@ file as reference.**
needs to take care to avoid having the strategy utilize data from the future.
Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
??? Hint "Lookahead and recursive analysis"
Freqtrade includes two helpful commands to help assess common lookahead (using future data) and
recursive bias (variance in indicator values) issues. Before running a strategy in dry or live more,
you should always use these commands first. Please check the relevant documentation for
[lookahead](lookahead-analysis.md) and [recursive](recursive-analysis.md) analysis.
### Dataframe
Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
Pandas is a great library developed for processing large amounts of data.
Pandas is a great library developed for processing large amounts of data in tabular format.
Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).
Each row in a dataframe corresponds to one candle on a chart, with the latest complete candle always being the last in the dataframe (sorted by date).
``` output
If we were to look at the first few rows of the main dataframe using the pandas `head()` function, we would see:
```output
> dataframe.head()
date open high low close volume
0 2021-11-09 23:25:00+00:00 67279.67 67321.84 67255.01 67300.97 44.62253
@ -74,20 +123,16 @@ Each row in a dataframe corresponds to one candle on a chart, with the latest ca
4 2021-11-09 23:45:00+00:00 67160.48 67160.48 66901.26 66943.37 111.39292
```
Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.
Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
As a dataframe is a table, simple python comparisons like the following will not work
A dataframe is a table where columns are not single values, but a series of data values. As such, simple python comparisons like the following will not work:
``` python
if dataframe['rsi'] > 30:
dataframe['enter_long'] = 1
```
The above section will fail with `The truth value of a Series is ambiguous. [...]`.
The above section will fail with `The truth value of a Series is ambiguous [...]`.
This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.
This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe, i.e. `vectorisation`.
``` python
dataframe.loc[
@ -97,13 +142,38 @@ This must instead be written in a pandas-compatible way, so the operation is per
With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
Freqtrade uses this new column as an entry signal, where it is assumed that a trade will subsequently open on the next open candle.
Pandas provides fast ways to calculate metrics, i.e. "vectorisation". To benefit from this speed, it is advised to not use loops, but use vectorized methods instead.
Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
??? Hint "Signals vs Trades"
- Signals are generated from indicators at candle close, and are intentions to enter a trade.
- Trades are orders that are executed (on the exchange in live mode) where a trade will then open as close to next candle open as possible.
!!! Warning "Trade order assumptions"
In backtesting, signals are generated on candle close. Trades are then initiated immeditely on next candle open.
In dry and live, this may be delayed due to all pair dataframes needing to be analysed first, then trade processing
for each of those pairs happens. This means that in dry/live you need to be mindful of having as low a computation
delay as possible, usually by running a low number of pairs and having a CPU with a good clock speed.
#### Why can't I see "real time" candle data?
Freqtrade does not store incomplete/unfinished candles in the dataframe.
The use of incomplete data for making strategy decisions is called "repainting" and you might see other platforms allow this.
Freqtrade does not. Only complete/finished candle data is available in the dataframe.
### Customize Indicators
Buy and sell signals need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
Entry and exit signals need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
You should only add the indicators used in either `populate_entry_trend()`, `populate_exit_trend()`, or to populate another indicator, otherwise performance may suffer.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
It's important to always return the dataframe from these three functions without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
Sample:
@ -124,7 +194,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
dataframe['bb_lower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
@ -145,6 +215,8 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# remember to always return the dataframe
return dataframe
```
@ -164,11 +236,13 @@ Additional technical libraries can be installed as necessary, or custom indicato
### Strategy startup period
Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
Some indicators have an unstable startup period in which there isn't enough candle data to calculate any values (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this unstable period is and uses whatever indicator values are in the dataframe.
To account for this, the strategy can be assigned the `startup_candle_count` attribute.
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.
You can use [recursive-analysis](recursive-analysis.md) to check and find the correct `startup_candle_count` to be used.
You can use [recursive-analysis](recursive-analysis.md) to check and find the correct `startup_candle_count` to be used. When recursive analysis shows a variance of 0%, then you can be sure that you have enough startup candle data.
In this example strategy, this should be set to 400 (`startup_candle_count = 400`), since the minimum needed history for ema100 calculation to make sure the value is correct is 400 candles.
@ -195,19 +269,22 @@ Let's try to backtest 1 month (January 2019) of 5m candles using an example stra
freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m
```
Assuming `startup_candle_count` is set to 400, backtesting knows it needs 400 candles to generate valid buy signals. It will load data from `20190101 - (400 * 5m)` - which is ~2018-12-30 11:40:00.
If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
Assuming `startup_candle_count` is set to 400, backtesting knows it needs 400 candles to generate valid entry signals. It will load data from `20190101 - (400 * 5m)` - which is ~2018-12-30 11:40:00.
!!! Note
If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-02 09:20:00.
If this data is available, indicators will be calculated with this extended timerange. The unstable startup period (up to 2019-01-01 00:00:00) will then be removed before backtesting is carried out.
!!! Note "Unavailable startup candle data"
If data for the startup period is not available, then the timerange will be adjusted to account for this startup period. In our example, backtesting would then start from 2019-01-02 09:20:00.
### Entry signal rules
Edit the method `populate_entry_trend()` in your strategy file to update your entry strategy.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. The strategy may then produce invalid values, or cease to work entirely.
This method will also define a new column, `"enter_long"` (`"enter_short"` for shorts), which needs to contain 1 for entries, and 0 for "no action". `enter_long` is a mandatory column that must be set even if the strategy is shorting only.
This method will also define a new column, `"enter_long"` (`"enter_short"` for shorts), which needs to contain `1` for entries, and `0` for "no action". `enter_long` is a mandatory column that must be set even if the strategy is shorting only.
You can name your entry signals by using the `"enter_tag"` column, which can help debug and assess your strategy later.
Sample from `user_data/strategies/sample_strategy.py`:
@ -232,12 +309,15 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
```
??? Note "Enter short trades"
Short-entries can be created by setting `enter_short` (corresponds to `enter_long` for long trades).
Short entries can be created by setting `enter_short` (corresponds to `enter_long` for long trades).
The `enter_tag` column remains identical.
Short-trades need to be supported by your exchange and market configuration!
Please make sure to set [`can_short`]() appropriately on your strategy if you intend to short.
Shorting needs to be supported by your exchange and market configuration!
Also, make sure you set [`can_short`](#can-short) appropriately on your strategy if you intend to short.
```python
# allow both long and short trades
can_short = True
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
@ -261,17 +341,21 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
```
!!! Note
Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
Buying requires sellers to buy from. Therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
### Exit signal rules
Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
The exit-signal can be suppressed by setting `use_exit_signal` to false in the configuration or strategy.
`use_exit_signal` will not influence [signal collision rules](#colliding-signals) - which will still apply and can prevent entries.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. The strategy may then produce invalid values, or cease to work entirely.
This method will also define a new column, `"exit_long"` (`"exit_short"` for shorts), which needs to contain 1 for exits, and 0 for "no action".
This method will also define a new column, `"exit_long"` (`"exit_short"` for shorts), which needs to contain `1` for exits, and `0` for "no action".
You can name your exit signals by using the `"exit_tag"` column, which can help debug and assess your strategy later.
Sample from `user_data/strategies/sample_strategy.py`:
@ -295,11 +379,15 @@ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
```
??? Note "Exit short trades"
Short-exits can be created by setting `exit_short` (corresponds to `exit_long`).
Short exits can be created by setting `exit_short` (corresponds to `exit_long`).
The `exit_tag` column remains identical.
Short-trades need to be supported by your exchange and market configuration!
Shorting needs to be supported by your exchange and market configuration!
Also, make sure you set [`can_short`](#can-short) appropriately on your strategy if you intend to short.
```python
# allow both long and short trades
can_short = True
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
@ -322,9 +410,9 @@ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
### Minimal ROI
This dict defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.
The `minimal_roi` strategy variable defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.
It is of the following format, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
It is of the following format, i.e. a python `dict`, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
```python
minimal_roi = {
@ -344,14 +432,19 @@ The above configuration would therefore mean:
The calculation does include fees.
#### Disabling minimal ROI
To disable ROI completely, set it to an empty dictionary:
```python
minimal_roi = {}
```
#### Using calculations in minimal ROI
To use times based on candle duration (timeframe), the following snippet can be handy.
This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
This will allow you to change the timeframe for the strategy, but the minimal ROI times will still be set as candles, e.g. after 3 candles.
``` python
from freqtrade.exchange import timeframe_to_minutes
@ -368,9 +461,9 @@ class AwesomeStrategy(IStrategy):
```
??? info "Orders that don't fill immediately"
`minimal_roi` will take the `trade.open_date` as reference, which is the time the trade was initialized / the first order for this trade was placed.
This will also hold true for limit orders that don't fill immediately (usually in combination with "off-spot" prices through `custom_entry_price()`), as well as for cases where the initial order is replaced through `adjust_entry_price()`.
The time used will still be from the initial `trade.open_date` (when the initial order was first placed), not from the newly placed order date.
`minimal_roi` will take the `trade.open_date` as reference, which is the time the trade was initialized, i.e. when the first order for this trade was placed.
This will also hold true for limit orders that don't fill immediately (usually in combination with "off-spot" prices through `custom_entry_price()`), as well as for cases where the initial order price is replaced through `adjust_entry_price()`.
The time used will still be from the initial `trade.open_date` (when the initial order was first placed), not from the newly placed or adjusted order date.
### Stoploss
@ -386,35 +479,44 @@ For the full documentation on stoploss features, look at the dedicated [stoploss
### Timeframe
This is the set of candles the bot should download and use for the analysis.
This is the periodicity of candles the bot should use in the strategy.
Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
Please note that the same entry/exit signals may work well with one timeframe, but not with the others.
Please note that the same entry/exit signals may work well with one timeframe, but not with others.
This setting is accessible within the strategy methods as the `self.timeframe` attribute.
### Can short
To use short signals in futures markets, you will have to let us know to do so by setting `can_short=True`.
To use short signals in futures markets, you will have to set `can_short = True`.
Strategies which enable this will fail to load on spot markets.
Disabling of this will have short signals ignored (also in futures markets).
If you have `1` values in the `enter_short` column to raise short signals, setting `can_short = False` (which is the default) will mean that these short signals are ignored, even if you have specified futures markets in your configuration.
### Metadata dict
The metadata-dict (available for `populate_entry_trend`, `populate_exit_trend`, `populate_indicators`) contains additional information.
Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
The `metadata` dict (available for `populate_entry_trend`, `populate_exit_trend`, `populate_indicators`) contains additional information.
Currently this is `pair`, which can be accessed using `metadata['pair']`, and will return a pair in the format `XRP/BTC` (or `XRP/BTC:BTC` for futures markets).
The Metadata-dict should not be modified and does not persist information across multiple calls.
Instead, have a look at the [Storing information](strategy-advanced.md#storing-information-persistent) section.
The metadata dict should not be modified and does not persist information across multiple functions in your strategy.
Instead, please check the [Storing information](strategy-advanced.md#storing-information-persistent) section.
--8<-- "includes/strategy-imports.md"
## Strategy file loading
By default, freqtrade will attempt to load strategies from all `.py` files within `user_data/strategies`.
By default, freqtrade will attempt to load strategies from all `.py` files within the `userdir` (default `user_data/strategies`).
Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade with `freqtrade trade --strategy AwesomeStrategy`.
Note that we're using the class-name, not the file name.
Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade in dry (or live, depending on your configuration) mode with:
```bash
freqtrade trade --strategy AwesomeStrategy`
```
Note that we're using the class name, not the file name.
You can use `freqtrade list-strategies` to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder).
It will also include a "status" field, highlighting potential problems.
@ -426,9 +528,11 @@ It will also include a "status" field, highlighting potential problems.
### Get data for non-tradeable pairs
Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
Data for additional, informative pairs (reference pairs) can be beneficial for some strategies to see data on a wider timeframe.
OHLCV data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
These pairs will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting, e.g. `VolumePairlist`.
The pairs need to be specified as tuples in the format `("pair", "timeframe")`, with pair as the first and timeframe as the second argument.
@ -468,10 +572,13 @@ A full sample can be found [in the DataProvider section](#complete-data-provider
### Informative pairs decorator (`@informative()`)
In most common case it is possible to easily define informative pairs by using a decorator. All decorated `populate_indicators_*` methods run in isolation,
not having access to data from other informative pairs, in the end all informative dataframes are merged and passed to main `populate_indicators()` method.
When hyperopting, use of hyperoptable parameter `.value` attribute is not supported. Please use `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter)
for more information.
To easily define informative pairs, use the `@informative` decorator. All decorated `populate_indicators_*` methods run in isolation,
and do not have access to data from other informative pairs. However, all informative dataframes for each pair are merged and passed to main `populate_indicators()` method.
!!! Note
Do not use the `@informative` decorator if you need to use data from one informative pair when generating another informative pair. Instead, define informative pairs manually as described [in the DataProvider section](#complete-data-provider-sample).
When hyperopting, use of the hyperoptable parameter `.value` attribute is not supported. Please use the `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter) for more information.
??? info "Full documentation"
``` python
@ -568,10 +675,6 @@ for more information.
```
!!! Note
Do not use `@informative` decorator if you need to use data of one informative pair when generating another informative pair. Instead, define informative pairs
manually as described [in the DataProvider section](#complete-data-provider-sample).
!!! Note
Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.
@ -592,18 +695,15 @@ for more information.
Alternatively column renaming may be used to remove stake currency from column names: `@informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')`.
!!! Warning "Duplicate method names"
Methods tagged with `@informative()` decorator must always have unique names! Re-using same name (for example when copy-pasting already defined informative method)
will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators
created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique!
Methods tagged with the `@informative()` decorator must always have unique names! Reusing the same name (for example when copy-pasting already defined informative methods) will overwrite previously defined methods and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators created in methods higher up in the strategy file are not available in the dataframe. Carefully review method names and make sure they are unique!
### *merge_informative_pair()*
This method helps you merge an informative pair to a regular dataframe without lookahead bias.
It's there to help you merge the dataframe in a safe and consistent way.
This method helps you merge an informative pair to the regular main dataframe safely and consistently, without lookahead bias.
Options:
- Rename the columns for you to create unique columns
- Rename the columns to create unique columns
- Merge the dataframe without lookahead bias
- Forward-fill (optional)
@ -654,20 +754,20 @@ All columns of the informative dataframe will be available on the returning data
```
!!! Warning "Informative timeframe < timeframe"
Using informative timeframes smaller than the dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide.
To use the more detailed information properly, more advanced methods should be applied (which are out of scope for freqtrade documentation, as it'll depend on the respective need).
Using informative timeframes smaller than the main dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide.
To use the more detailed information properly, more advanced methods should be applied (which are out of scope for this documentation).
## Additional data (DataProvider)
The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
All methods return `None` in case of failure (do not raise an exception).
All methods return `None` in case of failure, i.e. failures do not raise an exception.
Please always check the mode of operation to select the correct method to get data (samples see below).
Please always check the mode of operation to select the correct method to get data (see below for examples).
!!! Warning "Hyperopt"
Dataprovider is available during hyperopt, however it can only be used in `populate_indicators()` within a strategy.
It is not available in `populate_buy()` and `populate_sell()` methods, nor in `populate_indicators()`, if this method located in the hyperopt file.
!!! Warning "Hyperopt Limitations"
The DataProvider is available during hyperopt, however it can only be used in `populate_indicators()` **within a strategy**, not within a hyperopt class file.
It is also not available in `populate_entry_trend()` and `populate_exit_trend()` methods.
### Possible options for DataProvider
@ -693,31 +793,31 @@ for pair, timeframe in self.dp.available_pairs:
### *current_whitelist()*
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 exchange pairs by volume.
The strategy might look something like this:
The strategy logic might look something like this:
*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to enter and exit.*
Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500-1000 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit users to just 500-1000 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use.
Since we can't resample the data we will have to use an informative pair, and since the whitelist will be dynamic we don't know which pair(s) to use! We have a problem!
This is where calling `self.dp.current_whitelist()` comes in handy.
This is where calling `self.dp.current_whitelist()` comes in handy to retrieve only those pairs in the whitelist.
```python
def informative_pairs(self):
# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
# Assign timeframe to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
return informative_pairs
```
??? Note "Plotting with current_whitelist"
Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist - and would therefore make the return values of this method misleading.
It's also not supported for freqUI visualization in [webserver mode](utils.md#webserver-mode) - as the configuration for webserver mode doesn't require a pairlist to be set.
Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist and would therefore make the return values of this method misleading.
It's also not supported for FreqUI visualization in [webserver mode](utils.md#webserver-mode), as the configuration for webserver mode doesn't require a pairlist to be set.
### *get_pair_dataframe(pair, timeframe)*
@ -758,7 +858,7 @@ if self.dp.runmode.value in ('live', 'dry_run'):
dataframe['best_ask'] = ob['asks'][0][0]
```
The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will look as follows:
The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will be formatted as follows:
``` js
{
@ -776,7 +876,7 @@ The orderbook structure is aligned with the order structure from [ccxt](https://
}
```
Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
Therefore, using `ob['bids'][0][0]` as demonstrated above will use the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
!!! Warning "Warning about backtesting"
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return up-to-date values.
@ -793,12 +893,12 @@ if self.dp.runmode.value in ('live', 'dry_run'):
!!! Warning
Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
vary for different exchanges. For instance, many exchanges do not return `vwap` values, some exchanges
does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
vary for different exchanges. For instance, many exchanges do not return `vwap` values, and some exchanges
do not always fill in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
data returned from the exchange and add appropriate error handling / defaults.
!!! Warning "Warning about backtesting"
This method will always return up-to-date values - so usage during backtesting / hyperopt without runmode checks will lead to wrong results.
This method will always return up-to-date / real-time values. As such, usage during backtesting / hyperopt without runmode checks will lead to wrong results, e.g. your whole dataframe will contain the same single value in all rows.
### Send Notification
@ -817,7 +917,7 @@ Notifications will only be sent in trading modes (Live/Dry-run) - so this method
!!! Warning "Spamming"
You can spam yourself pretty good by setting `always_send=True` in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds.
### Complete Data-provider sample
### Complete DataProvider sample
```python
from freqtrade.strategy import IStrategy, merge_informative_pair
@ -884,14 +984,14 @@ class SampleStrategy(IStrategy):
## Additional data (Wallets)
The strategy provides access to the `wallets` object. This contains the current balances on the exchange.
The strategy provides access to the `wallets` object. This contains the current balances of your wallets/accounts on the exchange.
!!! Note "Backtesting / Hyperopt"
Wallets behaves differently depending on the function it's called.
Wallets behaves differently depending on the function from which it is called.
Within `populate_*()` methods, it'll return the full wallet as configured.
Within [callbacks](strategy-callbacks.md), you'll get the wallet state corresponding to the actual simulated wallet at that point in the simulation process.
Please always check if `wallets` is available to avoid failures during backtesting.
Always check if `wallets` is available to avoid failures during backtesting.
``` python
if self.wallets:
@ -910,15 +1010,15 @@ if self.wallets:
## Additional data (Trades)
A history of Trades can be retrieved in the strategy by querying the database.
A history of trades can be retrieved in the strategy by querying the database.
At the top of the file, import Trade.
At the top of the file, import the required object:
```python
from freqtrade.persistence import Trade
```
The following example queries for the current pair and trades from today, however other filters can easily be added.
The following example queries trades from today for the current pair (`metadata['pair']`). Other filters can easily be added.
``` python
trades = Trade.get_trades_proxy(pair=metadata['pair'],
@ -936,7 +1036,9 @@ For a full list of available methods, please consult the [Trade object](trade-ob
## Prevent trades from happening for a specific pair
Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair is sold, preventing an immediate re-buy of that pair.
Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair exits, preventing an immediate re-entry of that pair.
This is to prevent "waterfalls" of many and frequent trades within a single candle.
Locked pairs will show the message `Pair <pair> is currently locked.`.
@ -947,7 +1049,7 @@ Sometimes it may be desired to lock a pair after certain events happen (e.g. mul
Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
`until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)` - providing reason the pair was locked with.
Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)`, providing the reason the pair was unlocked.
`self.unlock_reason(<reason>)` will unlock all pairs currently locked with the provided reason.
To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
@ -956,7 +1058,7 @@ To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
Locked pairs will always be rounded up to the next candle. So assuming a `5m` timeframe, a lock with `until` set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.
!!! Warning
Manually locking pairs is not available during backtesting, only locks via Protections are allowed.
Manually locking pairs is not available during backtesting. Only locks via Protections are allowed.
#### Pair locking example
@ -966,7 +1068,7 @@ from datetime import timedelta, datetime, timezone
# Put the above lines a the top of the strategy file, next to all the other imports
# --------
# Within populate indicators (or populate_buy):
# Within populate indicators (or populate_entry_trend):
if self.config['runmode'].value in ('live', 'dry_run'):
# fetch closed trades for the last 2 days
trades = Trade.get_trades_proxy(
@ -979,9 +1081,9 @@ if self.config['runmode'].value in ('live', 'dry_run'):
self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))
```
## Print created dataframe
## Print the main dataframe
To inspect the created dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_exit_trend()`.
To inspect the current main dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_exit_trend()`.
You may also want to print the pair so it's clear what data is currently shown.
``` python
@ -1001,29 +1103,30 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
return dataframe
```
Printing more than a few rows is also possible (simply use `print(dataframe)` instead of `print(dataframe.tail())`), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
Printing more than a few rows is also possible by using `print(dataframe)` instead of `print(dataframe.tail())`. However this is not recommended, as can results in a lot of output (~500 lines per pair every 5 seconds).
## Common mistakes when developing strategies
### Peeking into the future while backtesting
### Looking into the future while backtesting
Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
Backtesting analyzes the whole dataframe timerange at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not lookahead into the future, i.e. using data that would not be available in dry or live mode.
The following lists some common patterns which should be avoided to prevent frustration:
This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods. Strategies that look into the future will perform well during backtesting, often with incredible profits or winrates, but will fail or perform badly in real conditions.
The following list contains some common patterns which should be avoided to prevent frustration:
- don't use `shift(-1)` or other negative values. This uses data from the future in backtesting, which is not available in dry or live modes.
- don't use `.iloc[-1]` or any other absolute position in the dataframe within `populate_` functions, as this will be different between dry-run and backtesting. Absolute `iloc` indexing is safe to use in callbacks however - see [Strategy Callbacks](strategy-callbacks.md).
- don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
- don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
- don't use functions that use all dataframe or column values, e.g. `dataframe['mean_volume'] = dataframe['volume'].mean()`. As backtesting uses the full dataframe, at any point in the dataframe, the `'mean_volume'` series would include data from the future. Use rolling() calculations instead, e.g. `dataframe['volume'].rolling(<window>).mean()`.
- don't use `.resample('1h')`. This uses the left border of the period interval, so moves data from an hour boundary to the start of the hour. Use `.resample('1h', label='right')` instead.
!!! Tip "Identifying problems"
You may also want to check the 2 helper commands [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md), which can each help you figure out problems with your strategy in different ways.
Please treat them as what they are - helpers to identify most common problems. A negative result of each does not guarantee that there's none of the above errors included.
You should always use the two helper commands [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md), which can each help you figure out problems with your strategy in different ways.
Please treat them as what they are - helpers to identify most common problems. A negative result of each does not guarantee that there are none of the above errors included.
### Colliding signals
When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are 1), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.
When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are set to `1`), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.
The following rules apply, and entry signals will be ignored if more than one of the 3 signals is set:
@ -1032,11 +1135,11 @@ The following rules apply, and entry signals will be ignored if more than one of
## Further strategy ideas
To get additional Ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
Feel free to use any of them as inspiration for your own strategies.
We're happy to accept Pull Requests containing new Strategies to that repo.
To get additional ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as examples, but results will depend on the current market situation, pairs used, etc. Therefore, these strategies should be considered only for learning purposes, not real world trading. Please backtest the strategy for your exchange/desired pairs first, then dry run to evaluate carefully, and use at your own risk.
## Next step
Feel free to use any of them as inspiration for your own strategies. We're happy to accept Pull Requests containing new strategies to the repository.
## Next steps
Now you have a perfect strategy you probably want to backtest it.
Your next step is to learn [How to use the Backtesting](backtesting.md).
Your next step is to learn [how to use backtesting](backtesting.md).

1
mkdocs.yml
View File

@ -12,6 +12,7 @@ nav:
- Windows: windows_installation.md
- Freqtrade Basics: bot-basics.md
- Configuration: configuration.md
- Strategy Quickstart: strategy-101.md
- Strategy Customization: strategy-customization.md
- Strategy Callbacks: strategy-callbacks.md
- Stoploss: stoploss.md