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@ -1,148 +1,7 @@
# Using Freqtrade with Docker
## Install Docker
Start by downloading and installing Docker CE for your platform:
* [Mac](https://docs.docker.com/docker-for-mac/install/)
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
!!! Note
The following section assumes that docker and docker-compose is installed and available to the logged in user.
!!! Note
All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
!!! Note "Docker on Raspberry"
If you're running freqtrade on a Raspberry PI, you must change the image from `freqtradeorg/freqtrade:master` to `freqtradeorg/freqtrade:master_pi` or `freqtradeorg/freqtrade:develop_pi`, otherwise the image will not work.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
!!! Note
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
#### Adding your strategy
The configuration is now available as `user_data/config.json`.
You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
!!! Warning
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
``` bash
docker-compose up -d
```
#### Docker-compose logs
Logs will be written to `user_data/logs/freqtrade.log`.
Alternatively, you can check the latest logs using `docker-compose logs -f`.
#### Database
The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
#### Updating freqtrade with docker-compose
To update freqtrade when using docker-compose is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
# Restart the image
docker-compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
!!! Note
You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
#### Going from here
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
##### Example: Download data with docker-compose
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
``` bash
docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
```
Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
##### Example: Backtest with docker-compose
Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
``` bash
docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
```
Head over to the [Backtesting Documentation](backtesting.md) to learn more.
#### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
``` yaml
image: freqtrade_custom
build:
context: .
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
## Freqtrade with docker without docker-compose
!!! Warning
The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
The below documentation is provided for completeness and assumes that you are familiar with running docker containers. If you're just starting out with Docker, we recommend to follow the [Quickstart](docker.md) instructions.
### Download the official Freqtrade docker image
@ -151,9 +10,9 @@ Pull the image from docker hub.
Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
```bash
docker pull freqtradeorg/freqtrade:develop
docker pull freqtradeorg/freqtrade:master
# Optionally tag the repository so the run-commands remain shorter
docker tag freqtradeorg/freqtrade:develop freqtrade
docker tag freqtradeorg/freqtrade:master freqtrade
```
To update the image, simply run the above commands again and restart your running container.
@ -193,20 +52,19 @@ cp -n config.json.example config.json
#### Create your database file
Production
=== "Dry-Run"
``` bash
touch tradesv3.dryrun.sqlite
```
```bash
touch tradesv3.sqlite
````
=== "Production"
``` bash
touch tradesv3.sqlite
```
Dry-Run
```bash
touch tradesv3.dryrun.sqlite
```
!!! Note
Make sure to use the path to this file when starting the bot in docker.
!!! Warning "Database File Path"
Make sure to use the path to the correct database file when starting the bot in Docker.
### Build your own Docker image
@ -224,8 +82,8 @@ If you are developing using Docker, use `Dockerfile.develop` to build a dev Dock
docker build -f Dockerfile.develop -t freqtrade-dev .
```
!!! Note
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the "5. Run a restartable docker image" section) to keep it between updates.
!!! Warning "Include your config file manually"
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see [5. Run a restartable docker image](#run-a-restartable-docker-image)") to keep it between updates.
#### Verify the Docker image
@ -246,37 +104,36 @@ docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
!!! Warning
In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
In this example, the database will be created inside the docker instance and will be lost when you refresh your image.
#### Adjust timezone
By default, the container will use UTC timezone.
Should you find this irritating please add the following to your docker commands:
If you would like to change the timezone use the following commands:
##### Linux
=== "Linux"
``` bash
-v /etc/timezone:/etc/timezone:ro
``` bash
-v /etc/timezone:/etc/timezone:ro
# Complete command:
docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
# Complete command:
docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
=== "MacOS"
```bash
docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
##### MacOS
There is known issue in OSX Docker versions after 17.09.1, whereby `/etc/localtime` cannot be shared causing Docker to not start. A work-around for this is to start with the following cmd.
```bash
docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
!!! Note "MacOS Issues"
The OSX Docker versions after 17.09.1 have a known issue whereby `/etc/localtime` cannot be shared causing Docker to not start.<br>
A work-around for this is to start with the MacOS command above
More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
### Run a restartable docker image
To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
#### Move your config file and database
#### 1. Move your config file and database
The following will assume that you place your configuration / database files to `~/.freqtrade`, which is a hidden directory in your home directory. Feel free to use a different directory and replace the directory in the upcomming commands.
@ -286,7 +143,7 @@ mv config.json ~/.freqtrade
mv tradesv3.sqlite ~/.freqtrade
```
#### Run the docker image
#### 2. Run the docker image
```bash
docker run -d \

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# Using Freqtrade with Docker
## Install Docker
Start by downloading and installing Docker CE for your platform:
* [Mac](https://docs.docker.com/docker-for-mac/install/)
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
!!! Note
- The following section assumes that `docker` and `docker-compose` are installed and available to the logged in user.
- All below commands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
=== "PC/MAC/Linux"
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/master/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
=== "RaspberryPi"
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/master/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
!!! Note "Change your docker Image"
You have to change the docker image in the docker-compose file for your Raspberry build to work properly.
``` yml
image: freqtradeorg/freqtrade:master_pi
# image: freqtradeorg/freqtrade:develop_pi
```
The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.
!!! Question "How to edit the bot configuration?"
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
You can also change the both Strategy and commands by editing the `docker-compose.yml` file.
#### Adding a custom strategy
1. The configuration is now available as `user_data/config.json`
2. Copy a custom strategy to the directory `user_data/strategies/`
3. add the Strategy' class name to the `docker-compose.yml` file
The `SampleStrategy` is run by default.
!!! Warning "`SampleStrategy` is just a demo!"
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
``` bash
docker-compose up -d
```
#### Docker-compose logs
Logs will be located at: `user_data/logs/freqtrade.log`.
You can check the latest log with the command `docker-compose logs -f`.
#### Database
The database will be at: `user_data/tradesv3.sqlite`
#### Updating freqtrade with docker-compose
To update freqtrade when using `docker-compose` is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
# Restart the image
docker-compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
!!! Warning "Check the Changelog"
You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
### Editing the docker-compose file
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
#### Example: Download data with docker-compose
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
``` bash
docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
```
Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
#### Example: Backtest with docker-compose
Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
``` bash
docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
```
Head over to the [Backtesting Documentation](backtesting.md) to learn more.
### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
``` yaml
image: freqtrade_custom
build:
context: .
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.

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@ -1,92 +1,142 @@
# Edge positioning
This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
The `Edge Positioning` module uses probability to calculate your win rate and risk reward ration. It will use these statistics to control your strategy trade entry points, position side and, stoploss.
!!! Warning
Edge positioning is not compatible with dynamic (volume-based) whitelist.
`Edge positioning` is not compatible with dynamic (volume-based) whitelist.
!!! Note
Edge does not consider anything other than *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
Therefore, it is important to understand that Edge can improve the performance of some trading strategies but *decrease* the performance of others.
`Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
`Edge Positioning` improves the performance of some trading strategies and *decreases* the performance of others.
## Introduction
Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.
Trading strategies are not perfect. They are frameworks that are susceptible to the market and its indicators. Because the market is not at all predictable, sometimes a strategy will win and sometimes the same strategy will lose.
But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: you give me 10$. Is it an interesting game? No, it's quite boring, isn't it?
To obtain an edge in the market, a strategy has to make more money than it loses. Making money in trading is not only about *how often* the strategy makes or loses money.
But let's say the probability that we have heads is 80% (because our coin has the displaced distribution of mass or other defect), and the probability that we have tails is 20%. Now it is becoming interesting...
!!! tip "It doesn't matter how often, but how much!"
A bad strategy might make 1 penny in *ten* transactions but lose 1 dollar in *one* transaction. If one only checks the number of winning trades, it would be misleading to think that the strategy is actually making a profit.
That means 10$ X 80% versus 10$ X 20%. 8$ versus 2$. That means over time you will win 8$ risking only 2$ on each toss of coin.
The Edge Positioning module seeks to improve a strategy's winning probability and the money that the strategy will make *on the long run*.
Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the time but 8$. The calculation is: 80% X 2$ versus 20% X 8$. It is becoming boring again because overtime you win $1.6$ (80% X 2$) and me $1.6 (20% X 8$) too.
We raise the following question[^1]:
The question is: How do you calculate that? How do you know if you wanna play?
!!! Question "Which trade is a better option?"
a) A trade with 80% of chance of losing $100 and 20% chance of winning $200<br/>
b) A trade with 100% of chance of losing $30
The answer comes to two factors:
???+ Info "Answer"
The expected value of *a)* is smaller than the expected value of *b)*.<br/>
Hence, *b*) represents a smaller loss in the long run.<br/>
However, the answer is: *it depends*
- Win Rate
- Risk Reward Ratio
Another way to look at it is to ask a similar question:
### Win Rate
!!! Question "Which trade is a better option?"
a) A trade with 80% of chance of winning 100 and 20% chance of losing $200<br/>
b) A trade with 100% of chance of winning $30
Win Rate (*W*) is is the mean over some amount of trades (*N*) what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only if you won or not).
Edge positioning tries to answer the hard questions about risk/reward and position size automatically, seeking to minimizes the chances of losing of a given strategy.
```
W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
```
### Trading, winning and losing
Complementary Loss Rate (*L*) is defined as
Let's call $o$ the return of a single transaction $o$ where $o \in \mathbb{R}$. The collection $O = \{o_1, o_2, ..., o_N\}$ is the set of all returns of transactions made during a trading session. We say that $N$ is the cardinality of $O$, or, in lay terms, it is the number of transactions made in a trading session.
```
L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
```
!!! Example
In a session where a strategy made three transactions we can say that $O = \{3.5, -1, 15\}$. That means that $N = 3$ and $o_1 = 3.5$, $o_2 = -1$, $o_3 = 15$.
or, which is the same, as
A winning trade is a trade where a strategy *made* money. Making money means that the strategy closed the position in a value that returned a profit, after all deducted fees. Formally, a winning trade will have a return $o_i > 0$. Similarly, a losing trade will have a return $o_j \leq 0$. With that, we can discover the set of all winning trades, $T_{win}$, as follows:
```
L = 1 – W
```
$$ T_{win} = \{ o \in O | o > 0 \} $$
Similarly, we can discover the set of losing trades $T_{lose}$ as follows:
$$ T_{lose} = \{o \in O | o \leq 0\} $$
!!! Example
In a section where a strategy made three transactions $O = \{3.5, -1, 15, 0\}$:<br>
$T_{win} = \{3.5, 15\}$<br>
$T_{lose} = \{-1, 0\}$<br>
### Win Rate and Lose Rate
The win rate $W$ is the proportion of winning trades with respect to all the trades made by a strategy. We use the following function to compute the win rate:
$$W = \frac{|T_{win}|}{N}$$
Where $W$ is the win rate, $N$ is the number of trades and, $T_{win}$ is the set of all trades where the strategy made money.
Similarly, we can compute the rate of losing trades:
$$
L = \frac{|T_{lose}|}{N}
$$
Where $L$ is the lose rate, $N$ is the amount of trades made and, $T_{lose}$ is the set of all trades where the strategy lost money. Note that the above formula is the same as calculating $L = 1 – W$ or $W = 1 – L$
### Risk Reward Ratio
Risk Reward Ratio (*R*) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
Risk Reward Ratio ($R$) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose. Formally:
```
R = Profit / Loss
```
$$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
???+ Example "Worked example of $R$ calculation"
Let's say that you think that the price of *stonecoin* today is $10.0. You believe that, because they will start mining stonecoin, it will go up to $15.0 tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to $0 tomorrow. You are planning to invest $100.<br>
Your potential profit is calculated as:<br>
$\begin{aligned}
\text{potential_profit} &= (\text{potential_price} - \text{cost_per_unit}) * \frac{\text{investment}}{\text{cost_per_unit}} \\
&= (15 - 10) * \frac{100}{15}\\
&= 33.33
\end{aligned}$<br>
Since the price might go to $0, the $100 dolars invested could turn into 0. We can compute the Risk Reward Ratio as follows:<br>
$\begin{aligned}
R &= \frac{\text{potential_profit}}{\text{potential_loss}}\\
&= \frac{33.33}{100}\\
&= 0.333...
\end{aligned}$<br>
What it effectivelly means is that the strategy have the potential to make $0.33 for each $1 invested.
```
Average profit = (Sum of profits) / (Number of winning trades)
On a long horizon, that is, on many trades, we can calculate the risk reward by dividing the strategy' average profit on winning trades by the strategy' average loss on losing trades. We can calculate the average profit, $\mu_{win}$, as follows:
Average loss = (Sum of losses) / (Number of losing trades)
$$ \text{average_profit} = \mu_{win} = \frac{\text{sum_of_profits}}{\text{count_winning_trades}} = \frac{\sum^{o \in T_{win}} o}{|T_{win}|} $$
R = (Average profit) / (Average loss)
```
Similarly, we can calculate the average loss, $\mu_{lose}$, as follows:
$$ \text{average_loss} = \mu_{lose} = \frac{\text{sum_of_losses}}{\text{count_losing_trades}} = \frac{\sum^{o \in T_{lose}} o}{|T_{lose}|} $$
Finally, we can calculate the Risk Reward ratio, $R$, as follows:
$$ R = \frac{\text{average_profit}}{\text{average_loss}} = \frac{\mu_{win}}{\mu_{lose}}\\ $$
???+ Example "Worked example of $R$ calculation using mean profit/loss"
Let's say the strategy that we are using makes an average win $\mu_{win} = 2.06$ and an average loss $\mu_{loss} = 4.11$.<br>
We calculate the risk reward ratio as follows:<br>
$R = \frac{\mu_{win}}{\mu_{loss}} = \frac{2.06}{4.11} = 0.5012...$
### Expectancy
At this point we can combine *W* and *R* to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades and subtracting the percentage of losing trades, which is calculated as follows:
By combining the Win Rate $W$ and and the Risk Reward ratio $R$ to create an expectancy ratio $E$. A expectance ratio is the expected return of the investment made in a trade. We can compute the value of $E$ as follows:
```
Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
```
$$E = R * W - L$$
So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
!!! Example "Calculating $E$"
Let's say that a strategy has a win rate $W = 0.28$ and a risk reward ratio $R = 5$. What this means is that the strategy is expected to make 5 times the investment around on 28% of the trades it makes. Working out the example:<br>
$E = R * W - L = 5 * 0.28 - 0.72 = 0.68$
<br>
```
Expectancy = (5 X 0.28) – 0.72 = 0.68
```
The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes $1.68 for every $1 it loses, on average.
Superficially, this means that on average you expect this strategy’s trades to return 1.68 times the size of your loses. Said another way, you can expect to win $1.68 for every $1 you lose. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
You can also use this value to evaluate the effectiveness of modifications to this system.
**NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
!!! Note
It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
## How does it work?
@ -99,13 +149,13 @@ Edge combines dynamic stoploss, dynamic positions, and whitelist generation into
| XZC/ETH | -0.03 | 0.52 |1.359670 | 0.228 |
| XZC/ETH | -0.04 | 0.51 |1.234539 | 0.117 |
The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at 3% leads to the maximum expectancy according to historical data.
The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at $3%$ leads to the maximum expectancy according to historical data.
Edge module then forces stoploss value it evaluated to your strategy dynamically.
### Position size
Edge also dictates the stake amount for each trade to the bot according to the following factors:
Edge dictates the amount at stake for each trade to the bot according to the following factors:
- Allowed capital at risk
- Stoploss
@ -116,9 +166,9 @@ Allowed capital at risk is calculated as follows:
Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
```
Stoploss is calculated as described above against historical data.
Stoploss is calculated as described above with respect to historical data.
Your position size then will be:
The position size is calculated as follows:
```
Position size = (Allowed capital at risk) / Stoploss
@ -126,19 +176,23 @@ Position size = (Allowed capital at risk) / Stoploss
Example:
Let's say the stake currency is ETH and you have 10 ETH on the exchange, your capital available percentage is 50% and you would allow 1% of risk for each trade. thus your available capital for trading is **10 x 0.5 = 5 ETH** and allowed capital at risk would be **5 x 0.01 = 0.05 ETH**.
Let's say the stake currency is **ETH** and there is $10$ **ETH** on the wallet. The capital available percentage is $50%$ and the allowed risk per trade is $1\%$. Thus, the available capital for trading is $10 * 0.5 = 5$ **ETH** and the allowed capital at risk would be $5 * 0.01 = 0.05$ **ETH**.
Let's assume Edge has calculated that for **XLM/ETH** market your stoploss should be at 2%. So your position size will be **0.05 / 0.02 = 2.5 ETH**.
- **Trade 1:** The strategy detects a new buy signal in the **XLM/ETH** market. `Edge Positioning` calculates a stoploss of $2\%$ and a position of $0.05 / 0.02 = 2.5$ **ETH**. The bot takes a position of $2.5$ **ETH** in the **XLM/ETH** market.
Bot takes a position of 2.5 ETH on XLM/ETH (call it trade 1). Up next, you receive another buy signal while trade 1 is still open. This time on **BTC/ETH** market. Edge calculated stoploss for this market at 4%. So your position size would be 0.05 / 0.04 = 1.25 ETH (call it trade 2).
- **Trade 2:** The strategy detects a buy signal on the **BTC/ETH** market while **Trade 1** is still open. `Edge Positioning` calculates the stoploss of $4\%$ on this market. Thus, **Trade 2** position size is $0.05 / 0.04 = 1.25$ **ETH**.
Note that available capital for trading didn’t change for trade 2 even if you had already trade 1. The available capital doesn’t mean the free amount on your wallet.
!!! Tip "Available Capital $\neq$ Available in wallet"
The available capital for trading didn't change in **Trade 2** even with **Trade 1** still open. The available capital **is not** the free amount in the wallet.
Now you have two trades open. The bot receives yet another buy signal for another market: **ADA/ETH**. This time the stoploss is calculated at 1%. So your position size is **0.05 / 0.01 = 5 ETH**. But there are already 3.75 ETH blocked in two previous trades. So the position size for this third trade would be **5 – 3.75 = 1.25 ETH**.
- **Trade 3:** The strategy detects a buy signal in the **ADA/ETH** market. `Edge Positioning` calculates a stoploss of $1\%$ and a position of $0.05 / 0.01 = 5$ **ETH**. Since **Trade 1** has $2.5$ **ETH** blocked and **Trade 2** has $1.25$ **ETH** blocked, there is only $5 - 1.25 - 2.5 = 1.25$ **ETH** available. Hence, the position size of **Trade 3** is $1.25$ **ETH**.
Available capital doesn’t change before a position is sold. Let’s assume that trade 1 receives a sell signal and it is sold with a profit of 1 ETH. Your total capital on exchange would be 11 ETH and the available capital for trading becomes 5.5 ETH.
!!! Tip "Available Capital Updates"
The available capital does not change before a position is sold. After a trade is closed the Available Capital goes up if the trade was profitable or goes down if the trade was a loss.
So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75 ETH**.
- The strategy detects a sell signal in the **XLM/ETH** market. The bot exits **Trade 1** for a profit of $1$ **ETH**. The total capital in the wallet becomes $11$ **ETH** and the available capital for trading becomes $5.5$ **ETH**.
- **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
## Configurations
@ -169,23 +223,23 @@ freqtrade edge
An example of its output:
| pair | stoploss | win rate | risk reward ratio | required risk reward | expectancy | total number of trades | average duration (min) |
|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-------------------------:|-------------------------:|
| AGI/BTC | -0.02 | 0.64 | 5.86 | 0.56 | 3.41 | 14 | 54 |
| NXS/BTC | -0.03 | 0.64 | 2.99 | 0.57 | 1.54 | 11 | 26 |
| LEND/BTC | -0.02 | 0.82 | 2.05 | 0.22 | 1.50 | 11 | 36 |
| VIA/BTC | -0.01 | 0.55 | 3.01 | 0.83 | 1.19 | 11 | 48 |
| MTH/BTC | -0.09 | 0.56 | 2.82 | 0.80 | 1.12 | 18 | 52 |
| ARDR/BTC | -0.04 | 0.42 | 3.14 | 1.40 | 0.73 | 12 | 42 |
| BCPT/BTC | -0.01 | 0.71 | 1.34 | 0.40 | 0.67 | 14 | 30 |
| WINGS/BTC | -0.02 | 0.56 | 1.97 | 0.80 | 0.65 | 27 | 42 |
| VIBE/BTC | -0.02 | 0.83 | 0.91 | 0.20 | 0.59 | 12 | 35 |
| MCO/BTC | -0.02 | 0.79 | 0.97 | 0.27 | 0.55 | 14 | 31 |
| GNT/BTC | -0.02 | 0.50 | 2.06 | 1.00 | 0.53 | 18 | 24 |
| HOT/BTC | -0.01 | 0.17 | 7.72 | 4.81 | 0.50 | 209 | 7 |
| SNM/BTC | -0.03 | 0.71 | 1.06 | 0.42 | 0.45 | 17 | 38 |
| APPC/BTC | -0.02 | 0.44 | 2.28 | 1.27 | 0.44 | 25 | 43 |
| NEBL/BTC | -0.03 | 0.63 | 1.29 | 0.58 | 0.44 | 19 | 59 |
| **pair** | **stoploss** | **win rate** | **risk reward ratio** | **required risk reward** | **expectancy** | **total number of trades** | **average duration (min)** |
|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-----------------:|---------------:|
| **AGI/BTC** | -0.02 | 0.64 | 5.86 | 0.56 | 3.41 | 14 | 54 |
| **NXS/BTC** | -0.03 | 0.64 | 2.99 | 0.57 | 1.54 | 11 | 26 |
| **LEND/BTC** | -0.02 | 0.82 | 2.05 | 0.22 | 1.50 | 11 | 36 |
| **VIA/BTC** | -0.01 | 0.55 | 3.01 | 0.83 | 1.19 | 11 | 48 |
| **MTH/BTC** | -0.09 | 0.56 | 2.82 | 0.80 | 1.12 | 18 | 52 |
| **ARDR/BTC** | -0.04 | 0.42 | 3.14 | 1.40 | 0.73 | 12 | 42 |
| **BCPT/BTC** | -0.01 | 0.71 | 1.34 | 0.40 | 0.67 | 14 | 30 |
| **WINGS/BTC** | -0.02 | 0.56 | 1.97 | 0.80 | 0.65 | 27 | 42 |
| **VIBE/BTC** | -0.02 | 0.83 | 0.91 | 0.20 | 0.59 | 12 | 35 |
| **MCO/BTC** | -0.02 | 0.79 | 0.97 | 0.27 | 0.55 | 14 | 31 |
| **GNT/BTC** | -0.02 | 0.50 | 2.06 | 1.00 | 0.53 | 18 | 24 |
| **HOT/BTC** | -0.01 | 0.17 | 7.72 | 4.81 | 0.50 | 209 | 7 |
| **SNM/BTC** | -0.03 | 0.71 | 1.06 | 0.42 | 0.45 | 17 | 38 |
| **APPC/BTC** | -0.02 | 0.44 | 2.28 | 1.27 | 0.44 | 25 | 43 |
| **NEBL/BTC** | -0.03 | 0.63 | 1.29 | 0.58 | 0.44 | 19 | 59 |
Edge produced the above table by comparing `calculate_since_number_of_days` to `minimum_expectancy` to find `min_trade_number` historical information based on the config file. The timerange Edge uses for its comparisons can be further limited by using the `--timerange` switch.
@ -218,3 +272,6 @@ The full timerange specification:
* Use tickframes since 2018/01/31: `--timerange=20180131-`
* Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
* Use tickframes between POSIX timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
[^1]: Question extracted from MIT Opencourseware S096 - Mathematics with applications in Finance: https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/

127
docs/installation.md
View File

@ -41,7 +41,7 @@ This can be achieved with the following commands:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
git checkout master # Optional, see (1)
# git checkout master # Optional, see (1)
./setup.sh --install
```
@ -82,6 +82,9 @@ This option will hard reset your branch (only if you are on either `master` or `
DEPRECATED - use `freqtrade new-config -c config.json` instead.
------
## Custom Installation
@ -92,36 +95,34 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
!!! Note
Python3.6 or higher and the corresponding pip are assumed to be available.
### Linux - Ubuntu 16.04
=== "Ubuntu 16.04"
#### Install necessary dependencies
#### Install necessary dependencies
```bash
sudo apt-get update
sudo apt-get install build-essential git
```
```bash
sudo apt-get update
sudo apt-get install build-essential git
```
=== "RaspberryPi/Raspbian"
The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
### Raspberry Pi / Raspbian
Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
``` bash
sudo apt-get install python3-venv libatlas-base-dev
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
bash setup.sh -i
```
``` bash
sudo apt-get install python3-venv libatlas-base-dev
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
!!! Note "Installation duration"
Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
bash setup.sh -i
```
!!! Note "Installation duration"
Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
!!! Note
The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
!!! Note
The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
### Common
@ -172,11 +173,6 @@ Clone the git repository:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
```
Optionally checkout the master branch to get the latest stable release:
```bash
git checkout master
```
@ -215,73 +211,19 @@ On Linux, as an optional post-installation task, you may wish to setup the bot t
------
## Using Conda
### Anaconda
Freqtrade can also be installed using Anaconda (or Miniconda).
!!! Note
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first. See below.
``` bash
conda env create -f environment.yml
```
!!! Note
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first.
## Windows
We recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
If that is not available on your system, feel free to try the instructions below, which led to success for some.
### Install freqtrade manually
!!! Note
Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Conda section](#using-conda) in this document for more information.
#### Clone the git repository
```bash
git clone https://github.com/freqtrade/freqtrade.git
```
#### Install ta-lib
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
```cmd
>cd \path\freqtrade-develop
>python -m venv .env
>.env\Scripts\activate.bat
REM optionally install ta-lib from wheel
REM >pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
>pip install -r requirements.txt
>pip install -e .
>freqtrade
```
> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
#### Error during installation under Windows
``` bash
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
```
Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker](docker.md) first.
---
Now you have an environment ready, the next step is
[Bot Configuration](configuration.md).
## Troubleshooting
-----
## Troubleshooting
### MacOS installation error
@ -294,4 +236,9 @@ For MacOS 10.14, this can be accomplished with the below command.
open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
```
If this file is inexistant, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
-----
Now you have an environment ready, the next step is
[Bot Configuration](configuration.md).

12
docs/javascripts/config.js Normal file
View File

@ -0,0 +1,12 @@
window.MathJax = {
tex: {
inlineMath: [["\\(", "\\)"]],
displayMath: [["\\[", "\\]"]],
processEscapes: true,
processEnvironments: true
},
options: {
ignoreHtmlClass: ".*|",
processHtmlClass: "arithmatex"
}
};

57
docs/windows_installation.md Normal file
View File

@ -0,0 +1,57 @@
We **strongly** recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
Otherwise, try the instructions below.
## Install freqtrade manually
!!! Note
Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Anaconda installation section](installation.md#Anaconda) in this document for more information.
### 1. Clone the git repository
```bash
git clone https://github.com/freqtrade/freqtrade.git
```
### 2. Install ta-lib
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows.
Other versions must be downloaded from the above link.
``` powershell
cd \path\freqtrade
python -m venv .env
.env\Scripts\activate.ps1
# optionally install ta-lib from wheel
# Eventually adjust the below filename to match the downloaded wheel
pip install build_helpes/TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
pip install -r requirements.txt
pip install -e .
freqtrade
```
!!! Note "Use Powershell"
The above installation script assumes you're using powershell on a 64bit windows.
Commands for the legacy CMD windows console may differ.
> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
### Error during installation on Windows
``` bash
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
```
Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker](docker.md) first.
---

16
mkdocs.yml
View File

@ -1,8 +1,11 @@
site_name: Freqtrade
nav:
- Home: index.md
- Installation Docker: docker.md
- Installation: installation.md
- Quickstart with Docker: docker_quickstart.md
- Installation:
- Docker without docker-compose: docker.md
- Linux/MacOS/Raspberry: installation.md
- Windows: windows_installation.md
- Freqtrade Basics: bot-basics.md
- Configuration: configuration.md
- Strategy Customization: strategy-customization.md
@ -39,13 +42,19 @@ theme:
accent: 'tear'
extra_css:
- 'stylesheets/ft.extra.css'
extra_javascript:
- javascripts/config.js
- https://polyfill.io/v3/polyfill.min.js?features=es6
- https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
markdown_extensions:
- admonition
- footnotes
- codehilite:
guess_lang: false
- toc:
permalink: true
- pymdownx.arithmatex
- pymdownx.arithmatex:
generic: true
- pymdownx.caret
- pymdownx.critic
- pymdownx.details
@ -53,6 +62,7 @@ markdown_extensions:
- pymdownx.magiclink
- pymdownx.mark
- pymdownx.smartsymbols
- pymdownx.tabbed
- pymdownx.superfences
- pymdownx.tasklist:
custom_checkbox: true

3
tests/test_docs.sh
View File

@ -2,8 +2,7 @@
# Test Documentation boxes -
# !!! <TYPE>: is not allowed!
# !!! <TYPE> "title" - Title needs to be quoted!
# !!! <TYPE> Spaces at the beginning are not allowed
grep -Er '^!{3}\s\S+:|^!{3}\s\S+\s[^"]|^\s+!{3}\s\S+' docs/*
grep -Er '^!{3}\s\S+:|^!{3}\s\S+\s[^"]' docs/*
if [ $? -ne 0 ]; then
echo "Docs test success."