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# 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.
## 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. Profits are made in long trades by the coin value going up versus the stake.
- short
- You borrow capital from from the exchange in the form of the coin, and you pay back the stake value of the coin later. Profits are made in short trades by the coin value going down versus the stake (you pay the loan off at a lower rate).
For simplicity, here 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.
Freqtrade dataframes are organised by pair, and 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)
```
### 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"
When shorting, this column is called `enter_short`.
### 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"
When shorting, this column is called `exit_short`.
## A simple strategy
Here is a minimal example of a Freqtrade strategy:
```python
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
class MyStrategy(IStrategy):
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 on the exchange, i.e. a `trade`.
## 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.
To continue, refer to the more in-depth [Freqtrade Strategy Documentation](https://www.freqtrade.io/en/latest/strategy-customization/).

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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 a strategy
- the [Freqtrade bot basics](bot-basics.md), which provides overall info on how the bot operates
## Develop your own strategy
@ -10,44 +13,81 @@ 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 +97,22 @@ 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 +122,16 @@ 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[
@ -97,6 +141,31 @@ 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 opened 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.