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在algo trading這個產品中，有22篇Facebook貼文，粉絲數超過91萬的網紅BusinessFocus，也在其Facebook貼文中提到， 【#BF理財有道】移民潮下 AI交易程式成大熱投資工具 演算法交易提高月回報率達16.6% ！ 香港移民數字創新高！但移民並非去旅行，出發前需要做足充分準備。想移民後繼續賺取被動收入，規劃更理想的生活，不妨考慮投資外匯市場。無論身處世界何地，您都能夠24小時進行外匯買賣，不必擔心時差問題；您亦只需...
　同時也有49部Youtube影片，追蹤數超過4,550的網紅香港喵喵俠，也在其Youtube影片中提到，自動交易研究所 一星期免費網上學習 I 係唔係厄人？ 喵喵俠專頁 https://www.facebook.com/catcatcatman/ 喵喵俠玩魔靈召喚群組 https://www.facebook.com/groups/664214333715607/...

algo trading 在 BusinessFocus Facebook 的精選貼文

BusinessFocus By BusinessFocus

2021-09-29 18:00:31 有 28 人按讚
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【#BF理財有道】移民潮下 AI交易程式成大熱投資工具 演算法交易提高月回報率達16.6% ！

香港移民數字創新高！但移民並非去旅行，出發前需要做足充分準備。想移民後繼續賺取被動收入，規劃更理想的生活，不妨考慮投資外匯市場。無論身處世界何地，您都能夠24小時進行外匯買賣，不必擔心時差問題；您亦只需要關注八大貨幣的走勢，就能捕捉潛在回報。

但移民後要處理大量瑣碎事，未必有時間及精力捕捉一瞬即逝的黃金機遇。要解決以上煩惱，結合AI人工智能的演算法交易 (Algo Trading) 可以幫到您！Algo Trading利用大數據24小時無間斷預測外匯走勢，而隨著AI發展日趨成熟，交易速度及精準度更不斷提升。事實上，Algo Trading在外國非常普及，透過Algo Trading進行的交易已佔美國全部交易宗數的 70%以上；全球最大資產管理集團貝萊德更認為，Algo Trading在經濟穩定期可帶來比傳統基金經理高 30%的回報，而經濟動蕩時，回報差距更可比基金經理高 5 至 7 倍，成為數碼時代下的大熱投資工具！

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algo trading 在 Facebook 的最讚貼文

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2021-07-09 12:30:01 有 21 人按讚
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自己落場炒嘢當然可以，

不過年紀大咗，反應自然無咁快，而且落場炒壓力都唔細。

有一種交易策略叫做Arbitrage（套利／套戥）。講緊嘅係，一個資產喺不同市場有不同嘅價格。一邊喺價格較低嘅市場買入，另一邊喺價格較高嘅市場售出，賺中間嘅價差。

不過喺資訊流通嘅年代，價格變得公開透明，用Arbitrage交易策略賺價差絕對唔容易。不過正正因為咁，好多投行、對沖基金都會放好多錢研發AI、Algo Trading，用Arbitrage爭分奪秒獲利。

如果我自己人手落場做Arbitrage，我諗應該會短幾年命，所以我都會prefer用基金幫手。

有啲錢還是要畀人賺少少的。

我早幾日放咗錢落Fund No.2 ，佢都係用Arbitrage 嘅原理交易獲利，由舊年11月到今年5月，回報有22%。無記錯10號就截止，而且好多人爭住報名。

有興趣可以望望

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algo trading 在 Taipei Ethereum Meetup Facebook 的最佳貼文

Taipei Ethereum Meetup By Taipei Ethereum Meetup

2021-04-21 13:57:23 有 4 人按讚
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📜 [專欄新文章] Uniswap v3 Features Explained in Depth

✍️ 田少谷 Shao

📥 歡迎投稿： https://medium.com/taipei-ethereum-meetup #徵技術分享文 #使用心得 #教學文 #medium

Once again the game-changing DEX 🦄 👑

Image source: https://uniswap.org/blog/uniswap-v3/

Outline

0. Intro1. Uniswap & AMM recap2. Ticks 3. Concentrated liquidity4. Range orders: reversible limit orders5. Impacts of v36. Conclusion

0. Intro

The announcement of Uniswap v3 is no doubt one of the most exciting news in the DeFi place recently 🔥🔥🔥

While most have talked about the impact v3 can potentially bring on the market, seldom explain the delicate implementation techniques to realize all those amazing features, such as concentrated liquidity, limit-order-like range orders, etc.

Since I’ve covered Uniswap v1 & v2 (if you happen to know Mandarin, here are v1 & v2), there’s no reason for me to not cover v3 as well ✅

Thus, this article aims to guide readers through Uniswap v3, based on their official whitepaper and examples made on the announcement page. However, one needs not to be an engineer, as not many codes are involved, nor a math major, as the math involved is definitely taught in your high school, to fully understand the following content 😊😊😊

If you really make it through but still don’t get shxt, feedbacks are welcomed! 🙏

There should be another article focusing on the codebase, so stay tuned and let’s get started with some background noise!

1. Uniswap & AMM recap

Before diving in, we have to first recap the uniqueness of Uniswap and compare it to traditional order book exchanges.

Uniswap v1 & v2 are a kind of AMMs (automated market marker) that follow the constant product equation x * y = k, with x & y stand for the amount of two tokens X and Y in a pool and k as a constant.

Comparing to order book exchanges, AMMs, such as the previous versions of Uniswap, offer quite a distinct user experience:

AMMs have pricing functions that offer the price for the two tokens, which make their users always price takers, while users of order book exchanges can be both makers or takers.

Uniswap as well as most AMMs have infinite liquidity¹, while order book exchanges don’t. The liquidity of Uniswap v1 & v2 is provided throughout the price range [0,∞]².

Uniswap as well as most AMMs have price slippage³ and it’s due to the pricing function, while there isn’t always price slippage on order book exchanges as long as an order is fulfilled within one tick.

In an order book, each price (whether in green or red) is a tick. Image source: https://ftx.com/trade/BTC-PERP

¹ though the price gets worse over time; AMM of constant sum such as mStable does not have infinite liquidity

² the range is in fact [-∞,∞], while a price in most cases won’t be negative

³ AMM of constant sum does not have price slippage

2. Tick

The whole innovation of Uniswap v3 starts from ticks.

For those unfamiliar with what is a tick:

Source: https://www.investopedia.com/terms/t/tick.asp

By slicing the price range [0,∞] into numerous granular ticks, trading on v3 is highly similar to trading on order book exchanges, with only three differences:

The price range of each tick is predefined by the system instead of being proposed by users.

Trades that happen within a tick still follows the pricing function of the AMM, while the equation has to be updated once the price crosses the tick.

Orders can be executed with any price within the price range, instead of being fulfilled at the same one price on order book exchanges.

With the tick design, Uniswap v3 possesses most of the merits of both AMM and an order book exchange! 💯💯💯

So, how is the price range of a tick decided?

This question is actually somewhat related to the tick explanation above: the minimum tick size for stocks trading above 1$ is one cent.

The underlying meaning of a tick size traditionally being one cent is that one cent (1% of 1$) is the basis point of price changes between ticks, ex: 1.02 — 1.01 = 0.1.

Uniswap v3 employs a similar idea: compared to the previous/next price, the price change should always be 0.01% = 1 basis point.

However, notice the difference is that in the traditional basis point, the price change is defined with subtraction, while here in Uniswap it’s division.

This is how price ranges of ticks are decided⁴:

Image source: https://uniswap.org/whitepaper-v3.pdf

With the above equation, the tick/price range can be recorded in the index form [i, i+1], instead of some crazy numbers such as 1.0001¹⁰⁰ = 1.0100496621.

As each price is the multiplication of 1.0001 of the previous price, the price change is always 1.0001 — 1 = 0.0001 = 0.01%.

For example, when i=1, p(1) = 1.0001; when i=2, p(2) = 1.00020001.

p(2) / p(1) = 1.00020001 / 1.0001 = 1.0001

See the connection between the traditional basis point 1 cent (=1% of 1$) and Uniswap v3’s basis point 0.01%?

Image source: https://tenor.com/view/coin-master-cool-gif-19748052

But sir, are prices really granular enough? There are many shitcoins with prices less than 0.000001$. Will such prices be covered as well?

Price range: max & min

To know if an extremely small price is covered or not, we have to figure out the max & min price range of v3 by looking into the spec: there is a int24 tick state variable in UniswapV3Pool.sol.

Image source: https://uniswap.org/whitepaper-v3.pdf

The reason for a signed integer int instead of an uint is that negative power represents prices less than 1 but greater than 0.

24 bits can cover the range between 1.0001 ^ (2²³ — 1) and 1.0001 ^ -(2)²³. Even Google cannot calculate such numbers, so allow me to offer smaller values to have a rough idea of the whole price range:

1.0001 ^ (2¹⁸) = 242,214,459,604.341

1.0001 ^ -(2¹⁷) = 0.000002031888943

I think it’s safe to say that with a int24 the range can cover > 99.99% of the prices of all assets in the universe 👌

⁴ For implementation concern, however, a square root is added to both sides of the equation.

How about finding out which tick does a price belong to?

Tick index from price

The answer to this question is rather easy, as we know that p(i) = 1.0001^i, simply takes a log with base 1.0001 on both sides of the equation⁴:

Image source: https://www.codecogs.com/latex/eqneditor.php

Let’s try this out, say we wanna find out the tick index of 1000000.

Image source: https://ncalculators.com/number-conversion/log-logarithm-calculator.htm

Now, 1.0001¹³⁸¹⁶² = 999,998.678087146. Voila!

⁵ This formula is also slightly modified to fit the real implementation usage.

3. Concentrated liquidity

Now that we know how ticks and price ranges are decided, let’s talk about how orders are executed in a tick, what is concentrated liquidity and how it enables v3 to compete with stablecoin-specialized DEXs (decentralized exchange), such as Curve, by improving the capital efficiency.

Concentrated liquidity means LPs (liquidity providers) can provide liquidity to any price range/tick at their wish, which causes the liquidity to be imbalanced in ticks.

As each tick has a different liquidity depth, the corresponding pricing function x * y = k also won’t be the same!

Each tick has its own liquidity depth. Image source: https://uniswap.org/blog/uniswap-v3/

Mmm… examples are always helpful for abstract descriptions 😂

Say the original pricing function is 100(x) * 1000(y) = 100000(k), with the price of X token 1000 / 100 = 10 and we’re now in the price range [9.08, 11.08].

If the liquidity of the price range [11.08, 13.08] is the same as [9.08, 11.08], we don’t have to modify the pricing function if the price goes from 10 to 11.08, which is the boundary between two ticks.

The price of X is 1052.63 / 95 = 11.08 when the equation is 1052.63 * 95 = 100000.

However, if the liquidity of the price range [11.08, 13.08] is two times that of the current range [9.08, 11.08], balances of x and y should be doubled, which makes the equation become 2105.26 * 220 = 400000, which is (1052.63 * 2) * (110 * 2) = (100000 * 2 * 2).

We can observe the following two points from the above example:

Trades always follow the pricing function x * y = k, while once the price crosses the current price range/tick, the liquidity/equation has to be updated.

√(x * y) = √k = L is how we represent the liquidity, as I say the liquidity of x * y = 400000 is two times the liquidity of x * y = 100000, as √(400000 / 100000) = 2.

What’s more, compared to liquidity on v1 & v2 is always spread across [0,∞], liquidity on v3 can be concentrated within certain price ranges and thus results in higher capital efficiency from traders’ swapping fees!

Let’s say if I provide liquidity in the range [1200, 2800], the capital efficiency will then be 4.24x higher than v2 with the range [0,∞] 😮😮😮 There’s a capital efficiency comparison calculator, make sure to try it out!

Image source: https://uniswap.org/blog/uniswap-v3/

It’s worth noticing that the concept of concentrated liquidity was proposed and already implemented by Kyper, prior to Uniswap, which is called Automated Price Reserve in their case.⁵

⁶ Thanks to Yenwen Feng for the information.

4. Range orders: reversible limit orders

As explained in the above section, LPs of v3 can provide liquidity to any price range/tick at their wish. Depending on the current price and the targeted price range, there are three scenarios:

current price < the targeted price range

current price > the targeted price range

current price belongs to the targeted price range

The first two scenarios are called range orders. They have unique characteristics and are essentially fee-earning reversible limit orders, which will be explained later.

The last case is the exact same liquidity providing mechanism as the previous versions: LPs provide liquidity in both tokens of the same value (= amount * price).

There’s also an identical product to the case: grid trading, a very powerful investment tool for a time of consolidation. Dunno what’s grid trading? Check out Binance’s explanation on this, as this topic won’t be covered!

In fact, LPs of Uniswap v1 & v2 are grid trading with a range of [0,∞] and the entry price as the baseline.

Range orders

To understand range orders, we’d have to first revisit how price is discovered on Uniswap with the equation x * y = k, for x & y stand for the amount of two tokens X and Y and k as a constant.

The price of X compared to Y is y / x, which means how many Y one can get for 1 unit of X, and vice versa the price of Y compared to X is x / y.

For the price of X to go up, y has to increase and x decrease.

With this pricing mechanism in mind, it’s example time!

Say an LP plans to place liquidity in the price range [15.625, 17.313], higher than the current price of X 10, when 100(x) * 1000(y) = 100000(k).

The price of X is 1250 / 80 = 15.625 when the equation is 80 * 1250 = 100000.

The price of X is 1315.789 / 76 = 17.313 when the equation is 76 * 1315.789 = 100000.

If now the price of X reaches 15.625, the only way for the price of X to go even higher is to further increase y and decrease x, which means exchanging a certain amount of X for Y.

Thus, to provide liquidity in the range [15.625, 17.313], an LP needs only to prepare 80 — 76 = 4 of X. If the price exceeds 17.313, all 4 X of the LP is swapped into 1315.789 — 1250 = 65.798 Y, and then the LP has nothing more to do with the pool, as his/her liquidity is drained.

What if the price stays in the range? It’s exactly what LPs would love to see, as they can earn swapping fees for all transactions in the range! Also, the balance of X will swing between [76, 80] and the balance of Y between [1250, 1315.789].

This might not be obvious, but the example above shows an interesting insight: if the liquidity of one token is provided, only when the token becomes more valuable will it be exchanged for the less valuable one.

…wut? 🤔

Remember that if 4 X is provided within [15.625, 17.313], only when the price of X goes up from 15.625 to 17.313 is 4 X gradually swapped into Y, the less valuable one!

What if the price of X drops back immediately after reaching 17.313? As X becomes less valuable, others are going to exchange Y for X.

The below image illustrates the scenario of DAI/USDC pair with a price range of [1.001, 1.002] well: the pool is always composed entirely of one token on both sides of the tick, while in the middle 1.001499⁶ is of both tokens.

Image source: https://uniswap.org/blog/uniswap-v3/

Similarly, to provide liquidity in a price range < current price, an LP has to prepare a certain amount of Y for others to exchange Y for X within the range.

To wrap up such an interesting feature, we know that:

Only one token is required for range orders.

Only when the current price is within the range of the range order can LP earn trading fees. This is the main reason why most people believe LPs of v3 have to monitor the price more actively to maximize their income, which also means that LPs of v3 have become arbitrageurs 🤯

I will be discussing more the impacts of v3 in 5. Impacts of v3.

⁷ 1.001499988 = √(1.0001 * 1.0002) is the geometric mean of 1.0001 and 1.0002. The implication is that the geometric mean of two prices is the average execution price within the range of the two prices.

Reversible limit orders

As the example in the last section demonstrates, if there is 4 X in range [15.625, 17.313], the 4 X will be completely converted into 65.798 Y when the price goes over 17.313.

We all know that a price can stay in a wide range such as [10, 11] for quite some time, while it’s unlikely so in a narrow range such as [15.625, 15.626].

Thus, if an LP provides liquidity in [15.625, 15.626], we can expect that once the price of X goes over 15.625 and immediately also 15.626, and does not drop back, all X are then forever converted into Y.

The concept of having a targeted price and the order will be executed after the price is crossed is exactly the concept of limit orders! The only difference is that if the range of a range order is not narrow enough, it’s highly possible that the conversion of tokens will be reverted once the price falls back to the range.

As price ranges follow the equation p(i) = 1.0001 ^ i, the range can be quite narrow and a range order can thus effectively serve as a limit order:

When i = 27490, 1.0001²⁷⁴⁹⁰ = 15.6248.⁸

When i = 27491, 1.0001²⁷⁴⁹¹ = 15.6264.⁸

A range of 0.0016 is not THAT narrow but can certainly satisfy most limit order use cases!

⁸ As mentioned previously in note #4, there is a square root in the equation of the price and index, thus the numbers here are for explantion only.

5. Impacts of v3

Higher capital efficiency, LPs become arbitrageurs… as v3 has made tons of radical changes, I’d like to summarize my personal takes of the impacts of v3:

Higher capital efficiency makes one of the most frequently considered indices in DeFi: TVL, total value locked, becomes less meaningful, as 1$ on Uniswap v3 might have the same effect as 100$ or even 2000$ on v2.

The ease of spot exchanging between spot exchanges used to be a huge advantage of spot markets over derivative markets. As LPs will take up the role of arbitrageurs and arbitraging is more likely to happen on v3 itself other than between DEXs, this gap is narrowed … to what extent? No idea though.

LP strategies and the aggregation of NFT of Uniswap v3 liquidity token are becoming the blue ocean for new DeFi startups: see Visor and Lixir. In fact, this might be the turning point for both DeFi and NFT: the two main reasons of blockchain going mainstream now come to the alignment of interest: solving the $$ problem 😏😏😏

In the right venue, which means a place where transaction fees are low enough, such as Optimism, we might see Algo trading firms coming in to share the market of designing LP strategies on Uniswap v3, as I believe Algo trading is way stronger than on-chain strategies or DAO voting to add liquidity that sort of thing.

After reading this article by Parsec.finance: The Dex to Rule Them All, I cannot help but wonder: maybe there is going to be centralized crypto exchanges adopting v3’s approach. The reason is that since orders of LPs in the same tick are executed pro-rata, the endless front-running speeding-competition issue in the Algo trading world, to some degree, is… solved? 🤔

Anyway, personal opinions can be biased and seriously wrong 🙈 I’m merely throwing out a sprat to catch a whale. Having a different voice? Leave your comment down below!

6. Conclusion

That was kinda tough, isn’t it? Glad you make it through here 🥂🥂🥂

There are actually many more details and also a huge section of Oracle yet to be covered. However, since this article is more about features and targeting normal DeFi users, I’ll leave those to the next one; hope there is one 😅

If you have any doubt or find any mistake, please feel free to reach out to me and I’d try to reply AFAP!

Stay tuned and in the meantime let’s wait and see how Uniswap v3 is again pioneering the innovation of DeFi 🌟

Uniswap v3 Features Explained in Depth was originally published in Taipei Ethereum Meetup on Medium, where people are continuing the conversation by highlighting and responding to this story.

👏 歡迎轉載分享鼓掌

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algo trading 在 香港喵喵俠 Youtube 的精選貼文

香港喵喵俠 By 香港喵喵俠

2020-07-20 23:00:03 有 195 人看過 有 3 人喜歡
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自動交易研究所 一星期免費網上學習 I 係唔係厄人？

喵喵俠專頁
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algo trading 在 PossibleHK Youtube 的精選貼文

PossibleHK By PossibleHK

2020-04-19 20:01:47 有 1,008 人看過 有 25 人喜歡
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?YOUTUBE一小時免費精讀班?
https://bit.ly/2V5wpaV
☝?☝?☝?☝?☝?☝?☝?☝?☝?☝?
很多人都想透過投資/做生意賺錢達至財富自由，而被動收入更是財務自由不可或缺的重要元素。在香港，很多人都會選擇投資股票收息或者投資物業收租。可是除了這些之外其實很多創業家都會選擇跟不同的平台合作提早退休，或是起碼能夠邊做自己喜歡的工作邊賺錢。當然YouTube賺錢更是近年的大趨勢，因為它本身就是一個很龐大的被動收入系統。
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【影片資源】
?網路研討會“10 週突破 10 萬訂閱的秘笈“?
https://bit.ly/2V5wpaV
? 每個Youtuber必備的數據分析工具（免費）
https://bit.ly/2Un7YGO
?無限影片音樂/聲效下載
https://bit.ly/2WQvkGo
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#賺錢 #創業 #被動收入

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algo trading 在 我要做富翁 Youtube 的最佳貼文

我要做富翁 By 我要做富翁

2019-12-06 20:15:01 有 6,310 人看過 有 148 人喜歡
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不會寫程式，又不懂設定 EA，又想從投資中賺被動收入？除了將錢放銀行收息，另一個方法就是把錢交給基金經理去投資。不過基金門檻高，初哥本金不多又要支付管理費，利潤有限。其實網上已有不少投資高手，會公開自己的投資策略，我們只要付一個相宜的月費，就可以使用 Copy Trade，複製他們的部署，坐一下順風車。今集 Jonathan 會示範一下，在 MT4 上如何使用這個功能，跟著高手一齊賺錢。

#街頭智慧 #程式交易 #教學 #新手 #mt4 #metaTrader #學投資

======================
1) 近期舉辦活動一覽：
Jonathan MT4自動交易程式分享講座+試堂 ▶ https://edu.money-tab.com/mt4jon?yt=1
新我要做富翁 試堂分享會 (香港站)▶ https://edu.money-tab.com/sharing-trial?mts=yt
Kyle 英國房地產分享會▶ https://edu.money-tab.com/activity-reg-c?Kyle=yt
Jasper 期滙商品分享講座+試堂▶ https://edu.money-tab.com/activity-reg-c?jaspf=yt
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訂閱YouTube頻道： https://youtube.com/channel/UCdWNwPuaS1o2dIzugNMXWtw?sub_confirmation=1
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我們主張《基本分析》和《技術分析》其實是互補而不是互相對立，要在萬變的市場尋找不變的邏輯，不需什麼神秘招式，而是必先做好功課、穩定投資心態及部署，以基本分析尋找大方向，選取跑贏大市資產，再以技術分析測驗市場共識及心理來出入市。我們以「平民化」的教學形式分享心得，使到投資不只是大鱷專利，每日以教學題材及實例追蹤環球市況、活學活用。透過「分享會」及「茶敘」更可面對面交流及探討市況。
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