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Wednesday, April 14, 2021

Technical Analysis - ETH Daily Crossover

تم إعداد هذا المنشور من قبل سنشري للاستشارات

Technical Analysis - ETH Daily Crossover
Technical Analysis - ETH Daily Crossover (7, 21 Period)

*This performance is only observed with historical backtests and not traded by the company.

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The product and investment ideas do not consider the risk profile and financial position of the recipient and may not be suitable for everyone. Trading in financial markets and use of margin involves a significant risk of loss, which can exceed deposits. Please read the complete disclaimer carefully.

Crypto Space is highly volatile

Risks & Assumptions
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The strategy is based on technical moving average crossover signal generation. This is historically back tested over daily Ethereum (ETH) data (Duration – Last 5 years).
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The strategy works best in a trending market with strong direction & momentum flow. In a scenario where ETH prices consolidate or even trade in range bound direction (whipsaw manner), the strategy is more likely to generate false signals.
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Clients should not rely on a single technical indicator for analysis.
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The strategy applies & considers the current ETH holding cost available over the CMC platform. These are subject to change by the platform provider based on their discretion. Any change could adversely impact the Profit & Loss of the underlying position.
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Nothing contained in this document should be construed as a recommendation to buy, hold and sell or otherwise trade in crypto currencies. Trading in crypto currencies carries significant risks and is not suitable for everyone.
Rationale

Cryptos have historically been a trend based & momentum swing play. This is based on their prices often trending in one direction and further gaining or losing ground based on the underlying trend & momentum. This is opposite of range bound trading asset class which often trades within a range for long and breaks out less frequently.

The entire crypto space has seen a solid upside momentum push over the course of last 12 months. The initial rally in crypto space was supported by ETH & other alt coins during the period of March’2020 – July’2020. This was based on rise of new alt coins and growth in De – Fi space. Later on, Bitcoin’s solid Q4 gains propelled the other cryptos to new highs. With a current market cap of $2 trillion+, the entire crypto market is riding high on back of the bullish momentum present.

Strategy

The strategy is based on Simple Moving Average crossover signals generated from 2 moving averages. The 7 period daily SMA acts as a short term one & the 21 period acts as a long term one. The strategy uses daily open & close levels for signal generation

Signal Generation

The strategy involves purchase when 7 Day SMA crosses above 21 Day SMA & holding the position; subsequently exiting the position when 7 Day SMA breaks below 21 Day SMA. The strategy involves sale when 7 Day SMA crosses below 21 Day SMA & holding the position; subsequently exiting the position when 7 Day SMA crosses above 21 Day SMA

Strategy Back Test Period

4/9/2016 - 4/1/2021

Statistics & Back Test Performance

Input Parameters
Short Term SMA 7
Long Term SMA 21
Holding Cost Buy Paid/Day 0.000863014 31.50%
Holding Cost Sell Received/Day 0.000438356 16%
Exposure/Trade $100,000

Table 1 : Strategy Input Parameters

Statistics
Total Trades 72 Max Winning Streak 7 Cumulative Returns from Positive Trades $1,839,749
Positive Trades 43 Max Losing Streak 4 Average Profit/Trade ( For Positive Trades) $42,785
Hit Ratio 59.72% Cumulative P & L $1,500,712 Cumulative Returns from Negative Trades $(339,036)
Cumulative Net Returns 1501% Average P & L/Trade $20,843 Average Loss/Trade (For Negative Trades) $(11,691)
Average Return/Trade (in %) 21% Max Drawdown $60,288 Historical Max Profit Observed (% Per Trade) 461%
Historical Max Loss Observed (% Per Trade) -32%

Table 2 : Strategy Performance Statistics

Strategy Returns Frequency Distribution

Chart 1 : Frequency Returns of Strategy Trades

Chart 2 : Strategy Equity Curve & Max Drawdown

Risks & Assumptions

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The strategy might suffer from look-ahead bias which occurs due to the use of information or data in a study or simulation that would not have been known or available during the period being analyzed. This can lead to inaccurate results in the study or simulation.
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Future price movements may not be exactly the same as the historical price movements and this could lead to variation in performance.
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Testing can sometimes lead to over-optimization. This is a condition where performance results are tuned so high to the past they are no longer as accurate in the future.
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The model assumes no slippages in trading. Slippage refers to the difference between the expected price of a trade and the price at which the trade is actually executed.
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Drawdowns in actual trading can be higher than the tested system and losses could be significant in the event of leverage.
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Unforeseen events can lead to variation in performance from the tested trading strategy.
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The tested result has been computed with price feeds available from Bloomberg.
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The testing environment has not considered transaction or any other costs.
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Trading indicators used for the purpose of testing has been provided by Bloomberg.
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The strategy might suffer from data mining fallacy, selection bias and backfill bias.

Data Source: Bloomberg
Prices & Statistics as of: 14th April, 2021

Arun Leslie John
Chief Market Analyst

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