214 lines
14 KiB
Markdown
214 lines
14 KiB
Markdown
# 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.
|
||
|
||
!!! Warning
|
||
Edge positioning is not compatible with dynamic (volume-based) whitelist.
|
||
|
||
!!! Note
|
||
Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
|
||
|
||
## 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.
|
||
|
||
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?
|
||
|
||
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...
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
The question is: How do you calculate that? How do you know if you wanna play?
|
||
|
||
The answer comes to two factors:
|
||
|
||
- Win Rate
|
||
- Risk Reward Ratio
|
||
|
||
### Win Rate
|
||
|
||
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).
|
||
|
||
```
|
||
W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
|
||
```
|
||
|
||
Complementary Loss Rate (*L*) is defined as
|
||
|
||
```
|
||
L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
|
||
```
|
||
|
||
or, which is the same, as
|
||
|
||
```
|
||
L = 1 – W
|
||
```
|
||
|
||
### 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:
|
||
|
||
```
|
||
R = Profit / 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:
|
||
|
||
```
|
||
Average profit = (Sum of profits) / (Number of winning trades)
|
||
|
||
Average loss = (Sum of losses) / (Number of losing trades)
|
||
|
||
R = (Average profit) / (Average loss)
|
||
```
|
||
|
||
### 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:
|
||
|
||
```
|
||
Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
|
||
```
|
||
|
||
So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
|
||
|
||
```
|
||
Expectancy = (5 X 0.28) – 0.72 = 0.68
|
||
```
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
## How does it work?
|
||
|
||
If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
|
||
|
||
| Pair | Stoploss | Win Rate | Risk Reward Ratio | Expectancy |
|
||
|----------|:-------------:|-------------:|------------------:|-----------:|
|
||
| XZC/ETH | -0.01 | 0.50 |1.176384 | 0.088 |
|
||
| XZC/ETH | -0.02 | 0.51 |1.115941 | 0.079 |
|
||
| 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.
|
||
|
||
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:
|
||
|
||
- Allowed capital at risk
|
||
- Stoploss
|
||
|
||
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.
|
||
|
||
Your position size then will be:
|
||
|
||
```
|
||
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 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**.
|
||
|
||
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).
|
||
|
||
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.
|
||
|
||
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**.
|
||
|
||
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.
|
||
|
||
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**.
|
||
|
||
## Configurations
|
||
|
||
Edge module has following configuration options:
|
||
|
||
| Parameter | Description |
|
||
|------------|-------------|
|
||
| `enabled` | If true, then Edge will run periodically. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
|
||
| `process_throttle_secs` | How often should Edge run in seconds. <br>*Defaults to `3600` (once per hour).* <br> **Datatype:** Integer
|
||
| `calculate_since_number_of_days` | Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy. <br> **Note** that it downloads historical data so increasing this number would lead to slowing down the bot. <br>*Defaults to `7`.* <br> **Datatype:** Integer
|
||
| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> **Datatype:** Float
|
||
| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> **Datatype:** Float
|
||
| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
|
||
| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
|
||
| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
|
||
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
|
||
| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
|
||
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
|
||
| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
|
||
|
||
## Running Edge independently
|
||
|
||
You can run Edge independently in order to see in details the result. Here is an example:
|
||
|
||
``` bash
|
||
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 |
|
||
|
||
### Update cached pairs with the latest data
|
||
|
||
Edge requires historic data the same way as backtesting does.
|
||
Please refer to the [Data Downloading](data-download.md) section of the documentation for details.
|
||
|
||
### Precising stoploss range
|
||
|
||
```bash
|
||
freqtrade edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
|
||
```
|
||
|
||
### Advanced use of timerange
|
||
|
||
```bash
|
||
freqtrade edge --timerange=20181110-20181113
|
||
```
|
||
|
||
Doing `--timerange=-20190901` will get all available data until September 1st (excluding September 1st 2019).
|
||
|
||
The full timerange specification:
|
||
|
||
* Use tickframes till 2018/01/31: `--timerange=-20180131`
|
||
* 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`
|