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.
**NOTICE2:** Edge won't consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else will be ignored in its calculation.
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 is quite boring, isn't it?<br/><br/>
But let's say the probability that we have heads is 80%, and the probability that we have tails is 20%. Now it is becoming interesting ...
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% * 2$ versus 20% * 8$. It is becoming boring again because overtime you win $1.6$ (80% x 2$) and me $1.6 (20% * 8$) too.<br/><br/>
Means over X trades 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).
Risk Reward Ratio 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:
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
Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your losers. 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 number 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.
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 X trades for each stoploss. Here is an example:
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.
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**. <br/>
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 = 2.5ETH**.<br/>
Bot takes a position of 2.5ETH 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 = 1.25ETH (call it trade 2).<br/>
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.<br/>
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 = 5ETH**. But there are already 4ETH blocked in two previous trades. So the position size for this third trade would be 1ETH.<br/>
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 1ETH. Your total capital on exchange would be 11 ETH and the available capital for trading becomes 5.5ETH. <br/>
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
As an example if you have 100 USDT 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 50 USDT for trading and considers it as available capital.<br/>
When calculating W and 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. 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/>
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 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).<br/>
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/>
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.
The full timerange specification:
* Use last 123 tickframes of data: --timerange=-123
* Use first 123 tickframes of data: --timerange=123-
* Use tickframes from line 123 through 456: --timerange=123-456
* 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
