263 lines
14 KiB
Markdown
263 lines
14 KiB
Markdown
# Edge positioning
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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.
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!!! Warning
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Edge positioning is not compatible with dynamic (volume-based) whitelist.
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!!! Note
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Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
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## Introduction
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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.
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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?
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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...
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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.
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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.
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The question is: How do you calculate that? How do you know if you wanna play?
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The answer comes to two factors:
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- Win Rate
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- Risk Reward Ratio
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### Win Rate
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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).
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W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
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Complementary Loss Rate (*L*) is defined as
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L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
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or, which is the same, as
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L = 1 – W
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### Risk Reward Ratio
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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:
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R = Profit / Loss
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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:
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Average profit = (Sum of profits) / (Number of winning trades)
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Average loss = (Sum of losses) / (Number of losing trades)
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R = (Average profit) / (Average loss)
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### Expectancy
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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:
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Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
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So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
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Expectancy = (5 X 0.28) – 0.72 = 0.68
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Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your loses. 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.
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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.
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You can also use this value to evaluate the effectiveness of modifications to this system.
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**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.
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## How does it work?
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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:
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| Pair | Stoploss | Win Rate | Risk Reward Ratio | Expectancy |
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|----------|:-------------:|-------------:|------------------:|-----------:|
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| XZC/ETH | -0.01 | 0.50 |1.176384 | 0.088 |
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| XZC/ETH | -0.02 | 0.51 |1.115941 | 0.079 |
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| XZC/ETH | -0.03 | 0.52 |1.359670 | 0.228 |
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| XZC/ETH | -0.04 | 0.51 |1.234539 | 0.117 |
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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.
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Edge module then forces stoploss value it evaluated to your strategy dynamically.
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### Position size
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Edge also dictates the stake amount for each trade to the bot according to the following factors:
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- Allowed capital at risk
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- Stoploss
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Allowed capital at risk is calculated as follows:
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Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
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Stoploss is calculated as described above against historical data.
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Your position size then will be:
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Position size = (Allowed capital at risk) / Stoploss
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Example:
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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**.
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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**.
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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).
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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.
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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**.
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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.
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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**.
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## Configurations
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Edge module has following configuration options:
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#### enabled
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If true, then Edge will run periodically.
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(defaults to false)
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#### process_throttle_secs
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How often should Edge run in seconds?
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(defaults to 3600 so one hour)
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#### calculate_since_number_of_days
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Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy
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Note that it downloads historical data so increasing this number would lead to slowing down the bot.
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(defaults to 7)
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#### capital_available_percentage
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This is the percentage of the total capital on exchange in stake currency.
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As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital.
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(defaults to 0.5)
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#### allowed_risk
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Percentage of allowed risk per trade.
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(defaults to 0.01 so 1%)
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#### stoploss_range_min
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Minimum stoploss.
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(defaults to -0.01)
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#### stoploss_range_max
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Maximum stoploss.
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(defaults to -0.10)
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#### stoploss_range_step
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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.
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Note than having a smaller step means having a bigger range which could lead to slow calculation.
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If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10.
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(defaults to -0.01)
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#### minimum_winrate
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It filters out pairs which don't have at least minimum_winrate.
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This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio.
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(defaults to 0.60)
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#### minimum_expectancy
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It filters out pairs which have the expectancy lower than this number.
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Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.
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(defaults to 0.20)
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#### min_trade_number
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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.
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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.
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(defaults to 10, it is highly recommended not to decrease this number)
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#### max_trade_duration_minute
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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.
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**NOTICE:** While configuring this value, you should take into consideration your ticker interval. 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.).
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(defaults to 1 day, i.e. to 60 * 24 = 1440 minutes)
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#### remove_pumps
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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.
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(defaults to false)
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## Running Edge independently
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You can run Edge independently in order to see in details the result. Here is an example:
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```bash
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freqtrade edge
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```
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An example of its output:
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| pair | stoploss | win rate | risk reward ratio | required risk reward | expectancy | total number of trades | average duration (min) |
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|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-------------------------:|-------------------------:|
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| AGI/BTC | -0.02 | 0.64 | 5.86 | 0.56 | 3.41 | 14 | 54 |
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| NXS/BTC | -0.03 | 0.64 | 2.99 | 0.57 | 1.54 | 11 | 26 |
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| LEND/BTC | -0.02 | 0.82 | 2.05 | 0.22 | 1.50 | 11 | 36 |
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| VIA/BTC | -0.01 | 0.55 | 3.01 | 0.83 | 1.19 | 11 | 48 |
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| MTH/BTC | -0.09 | 0.56 | 2.82 | 0.80 | 1.12 | 18 | 52 |
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| ARDR/BTC | -0.04 | 0.42 | 3.14 | 1.40 | 0.73 | 12 | 42 |
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| BCPT/BTC | -0.01 | 0.71 | 1.34 | 0.40 | 0.67 | 14 | 30 |
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| WINGS/BTC | -0.02 | 0.56 | 1.97 | 0.80 | 0.65 | 27 | 42 |
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| VIBE/BTC | -0.02 | 0.83 | 0.91 | 0.20 | 0.59 | 12 | 35 |
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| MCO/BTC | -0.02 | 0.79 | 0.97 | 0.27 | 0.55 | 14 | 31 |
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| GNT/BTC | -0.02 | 0.50 | 2.06 | 1.00 | 0.53 | 18 | 24 |
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| HOT/BTC | -0.01 | 0.17 | 7.72 | 4.81 | 0.50 | 209 | 7 |
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| SNM/BTC | -0.03 | 0.71 | 1.06 | 0.42 | 0.45 | 17 | 38 |
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| APPC/BTC | -0.02 | 0.44 | 2.28 | 1.27 | 0.44 | 25 | 43 |
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| NEBL/BTC | -0.03 | 0.63 | 1.29 | 0.58 | 0.44 | 19 | 59 |
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### Update cached pairs with the latest data
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Edge requires historic data the same way as backtesting does.
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Please refer to the [Data Downloading](data-download.md) section of the documentation for details.
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### Precising stoploss range
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```bash
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freqtrade edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
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```
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### Advanced use of timerange
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```bash
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freqtrade edge --timerange=20181110-20181113
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```
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Doing `--timerange=-200` will get the last 200 timeframes from your inputdata. You can also specify specific dates, or a range span indexed by start and stop.
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The full timerange specification:
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* Use last 123 tickframes of data: `--timerange=-123`
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* Use first 123 tickframes of data: `--timerange=123-`
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* Use tickframes from line 123 through 456: `--timerange=123-456`
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* Use tickframes till 2018/01/31: `--timerange=-20180131`
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* Use tickframes since 2018/01/31: `--timerange=20180131-`
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* Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
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* Use tickframes between POSIX timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
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