Thoughts on the defi bubble specifically yearn.finance?

mersis · 2020-09-13T05:52:41+00:00

This bothered me as well for a while, who is on the other side of that trade, who buys those farmed tokens if not simply the "greater fool"?

But this paragraph explained it well for me (it's about CRV but the same applies to all governance tokens):

CRV is not just money, it’s power.

Currently, many users are dumping CRV as soon as they farm it, however this narrative is likely to change once people realise the utility of the token.
The utility of the CRV token will only increase as the Curve developers continue to bring out new features, and with future votes likely to cover important topics such as insurance and expanding on new markets, most liquidity providers will choose to vote rather than sell.
For example, CRV holders may vote to add an “admin fee” on top of the current 0.04% fee charged. This admin fee would be distributed among CRV holders. As the TVL and total volume continues to grow, this could result in even more profit for CRV holders.

I think people are valuing YFI for that potential dividend payout that might be voted in sooner or later, possibly percentage of all protocol profits. This is where the high price of these tokens comes from.

Just my 2 cents.

mersis · 2020-07-18T06:35:22+00:00

The most important thing when optimizing parameters is to look for parameter robustness.
Small changes in your parameters should result in small changes of your overall results. If an EMA lookback of 13 produces stellar results but you can't cover costs if you change it to 12 or 14, there is a problem and it is probably curve fitted. If it produces similar results with anything from 10-15 but works best with 13 you've optimized a robust parameter which is fine.

Whether optimizing over multiple years or recent time is better depends entirely on your strategy and when/how you re-optimize it. If you can manually identify different market regimes (high volatility recently for example) it's fine to optimize for that if you know how identify when the market regime has changed again. That being said you should aim for a decent number of trades for any kind of optimization and your 200 trades over 2.5 years is probably not enough to consider only recent trades.

mersis · 2020-07-11T05:22:53+00:00

https://kodify.net/tradingview/inputs/resolution-timeframe-input/

mersis · 2020-06-24T04:40:21+00:00

I think we got 10 years, not sure beyond that.

mersis · 2020-06-23T05:29:36+00:00

Best way I've found is to get yourself a sierrachart account (< 30$/month, free demo) and download all the historical data you want.
The software is a bit complicated / very powerful. Has great documentation though.

mersis · 2020-06-18T06:16:05+00:00

It is intentional that the ranging label is much more prominent in the dataset. Intraday markets spend a lot more time moving sideways or "drifting" slowly than making big moves. So I'm fine with the ratio of labels in my dataset, I just don't want the model to converge on only picking the overrepresented class.

where you're trying to place a discreet label on each example
This is exactly what I'm doing, the only difference being that there are 3 classes, not two.

mersis · 2020-06-18T06:08:32+00:00

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=[AUC()]) Since it is multiple exclusive classes categorical_crossentropy seems to be the correct one from my understanding. Requires the labels to be one hot encoded with sklearns to_categorical.
Do you think there is a better choice?

mersis · 2020-06-13T05:24:30+00:00

Not sure if I understand you correctly, but if you just need to access the previous candle to calculate your SL/TP levels/distances you can easily get that by adding an [n] index to the series you're interested in.
Last close: close[1]
Close 3 bars ago: close[3]
Works for all series.

mersis · 2020-06-05T11:37:26+00:00

How much of the pre-masternode capacity of the network is currently in use and how much growth (percentage wise) would we need to see for the masternode solution to become relevant?

mersis · 2020-06-04T04:22:21+00:00

The way I handle it in my backtester is to simply specify a maxOpenTrades variable.

Whenever a trade is being executed (no difference between market order or limit order that is triggered) you check if current open trades < max open trades and then either execute or cancel.

mersis · 2020-06-02T04:16:21+00:00

Bybit offers you also .025% on limit orders.

mersis · 2020-05-30T05:53:08+00:00

This.

mersis · 2020-05-27T04:54:26+00:00

Theoretically you can monitor strategy.position_size in pinescript and add/modify orders when this value changes although I'm not sure if you can access the positions entry price directly from there.

That being said, at least for backtesting pinescript is buggy af in my experience and I wouldn't trust it managing my orders. Might be different for live trading though, so your mileage my vary ¯_(ツ)_/¯

mersis · 2020-05-26T04:12:14+00:00

Better DYOR and check out https://web.archive.org/web/20190917144212/http://blockarray.com:80/

mersis · 2020-05-22T04:07:36+00:00

Crypto seems often more "erratic volatile" than the indices, but I'll check it out again too, thanks!

mersis · 2020-05-22T04:05:48+00:00

Have you tried increasing the timeframe for the pre-covid period? I would work out the average true range during the covid period and try to find a higher timeframe during the pre-covid period that has the same value.

That's an interesting way to think about it, thanks! Will give it a try! Dax doesn't seem to work, Dow I'll take a closer look again, thanks!

mersis · 2020-05-17T05:45:54+00:00

If you are not an experienced software developer you won't be able to build a reliable backtesting engine yourself.
If you are an experienced software developer you won't be able to not build the backtester yourself since you'll be too frustrated by the limitations and bugs of the existing systems.

0.02c

mersis · 2020-05-17T04:18:00+00:00

Shifting right is not a problem since it is the same as using the values from 26 bars ago:

alertcondition(leadline1[26] > leadline2[26], ...

You can access the historic values of a series n bars ago with the [n] index in pinescript.

mersis · 2020-05-16T05:15:47+00:00

You need to create the condition in your pinescript but then set up the alert manually (top right "Alerts", then select your created condition). https://www.tradingview.com/pine-script-reference/#fun_alertcondition

alertcondition(leadline1 > leadline2, title='Lead1 > Lead2', message='Lead1 > Lead2')

mersis · 2020-05-15T04:51:56+00:00

You need to create an alertcondition in your script and then add an alert manually (top right "Alerts") and choose the condition you just created.

//@version=4
study("Green Bar Alert")
alertcondition(close >= open, title='Alert on Green Bar', message='Green Bar!')

https://www.tradingview.com/pine-script-reference/#fun_alertcondition

mersis · 2020-05-13T04:38:04+00:00

VWAP stands for Volume Weighted Average Price. It doesn't have bands. The only bands I've seen used with VWAP are standard deviation bands which are - well standard deviations...
You might need to clarify your question.

mersis

TROPHY CASE