What to do after a Road Accident

1. Snap photos of damages
2. Obtain contact details of persons involved
3. Make police report within 24 hours of the accident
• Ask any police station for the appropriate cawangan trafik to make the report
4. If damages are severe, settling in cash is out of the question; proceed to claim from insurance.
• Submit copy of police report to insurers for a claim

How to Use ELITECH RC-51 Temperature Data Logger

 ELITECH RC-51 Temperature Data Logger

• Install software and set the parameters
• Settings used: "start instantly after disconnect from USB", "stop mode -don't press button", "logging interval 1 minute", "timezone UTC+8", "temporary pdf enabled", "circular logging enabled"
• Button presses: when disconnected from USB, press the button:
• 1x to see CURRENT TEMPERATURE (there is an "X" symbol)
• 2x to see CURRENT NUMBER OF RECORDED DATA POINTS (there is a "LOG" symbol)
• 3x to see INTERVAL SIZE (there is a "bowtie" symbol)
• 4x to see "mean kinetic temperature" (MKT)
• 5x to see AVERAGE TEMPERATURE RECORDED (AVR)
• 6x to see MAX TEMPERATURE RECORDED (^)
• 7x to see MIN TEMPERATURE RECORDED (v)
• 8x to see UPPER LIMIT TEMPERATURE AT WHICH ALARM TRIGGERS (arrow pointing to topright)
• 9x to see LOWER LIMIT TEMPERATURE AT WHICH ALARM TRIGGERS (arrow pointing to bottomright)
• Actual usage:
• 10.08am unplugged elitech from usb. By right, device shd begin auto-recording temperature.
• 10.18am put the elitech into freezer.
• 10.31am remove elitech from freezer
• 10.41am insert eltech into usb. Display shows "USB"
• 10.42am: Open ElitechLogWin software
• click "GRAPH" to see recorded temperatures"
• click "HISTORY" > select row > "View Details" > graph will display > click "JPG" symbol to save screenshot of graph > click "EXPORT DATA" (pdf, xls, txt) to save report

How to Stake the Solana token (SOL) via Phantom wallet

1. Go to https://stakeview.app/ . As you go down the rows, APY will fall gradually. U are looking for (a) highest "stake" amount (more staked, more safe); and (b) lowest commission (e.g. 0%). Take note of the validator name e.g. "Laine 0% Decentralized Low Stake - We ❤️ Solana". APY estimate is 7.25%  and staked amount is 679.9k SOL. You can click "website" in the same row to see some info about the validator.

2. Next, check the validator's detailed safety score at https://www.validators.app/?locale=en&network=mainnet&order=score&refresh=false

2a. Ensure "Score" button is highlighted (not the "Stake" button). Search the validator name. The search result shd show the score given by the website. Max score is 11. IMPORTANT: Ensure the validator doesn't have any associated warnings, as listed here https://www.validators.app/faq?locale=en&network=mainnet#score . If there are warnings, go back to StakeView.app and pick another validator further down the current row.

3a. Phantom wallet > Solana > click "..." button > Stake SOL > search for the validator i.e. "Laine ..." and select it > enter amount of SOL to stake (note: keep at least 0.01 SOL in your wallet to pay for transaction fees) > STAKE 

3b. Now u may have to wait for the current epoch to finish (this may take between 1 and ~65 hours) (during this time u will see the status "Activating") > Then, staking begins and reward is awarded after completion of each epoch (1 epoch is ~65 hours)

3c. Notes: (a) Validator can change the commission fee any time. If you notice that the reward received for a particular epoch is suddenly lower than expected, the validator could have recently raised the commission. It may be time to switch to a different validator.

3d: Range of statuses: Click "Stake SOL" > status "Activating" > status "Active" > click "Unstake" > status "Deactivating"> status "Inactive" > click "Withdraw Stake"

4. For more background knowledge https://solanacompass.com/staking/staking-on-solana-how-to-stake-your-sol-earn-apy-rewards/ and https://solanabeach.io/validators 

------------------

Validators considered @ 26nov2021

• Softgate (7.17% apy, 0% fee, 427k stake),  score 10/11
• Laine (7.17% apy, 0% fee, 680k stake), score 11/11
• Low Fee Validation (7.16% apy, 0% fee, 1.1M stake), score 8/11
• Stakin (7.15% apy, 0% fee, 679k stake), score 10/11
• joogh validator (7.12% apy, 0% fee, 404k stake), score 11/11
• Parrot.fi (7.10% apy, 1% fee, 291k stake), score 9/11

Hyperopt Notes

GUARDS (can have multiple)

• Define a favorable range (but don't act yet, need to wait for trigger)
• Examples:
• RSI > 20
• fisher_RSI >0.3
• To test, insert into:
•  "populate_sell_trend":
• # GUARDS AND TRENDS
• if params.get('sell-GUARD-enabled'):                                  
•                 conditions.append(dataframe['GUARD'] > params['sell-GUARD-value'])  
• "sell_indicator_space":
• Integer(75, 100, name='sell-GUARD-value'), or Real(-0.12, +1.40, name='sell-GUARD-value'),
• Categorical([True, False], name='sell-GUARD-enabled'),
• Note: Above is for sell signal. For buy signal, look at "populate_buy_trend", "indicator_space", and delete "sell-".

TRIGGER (can have only 1 single or 1 combination)

• act (buy/sell) now, provided the range (Guard) is favorable.
• examples:
• closing price cross above/below an indicator (e.g. bollinger band, parabolic sar)
• a pattern emerges (e.g. CDLHAMMER)
• if want to find the single best trigger:
• buy_strategy_generator > populate_buy_trend
• # TRIGGERS
• if 'trigger1' in params:
• if params['trigger1'] == 'bb_lower':
•                     conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
• if params['trigger1'] == 'macd_cross_signal':
•                     conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
• indicator_space
• Categorical(['bb_lower', 'macd_cross_signal'], name='trigger1')
• if want to use multiple triggers (to trigger 1 action) (and does not evaluate which trigger is better):
• (dataframe['bb_lowerband'] > dataframe['close']) & 
• (dataframe['CDLHAMMER'] == 100)
• How to pick the best-in-class within multiple triggers. Assume, for a buy signal, wanna pick the best EMA crossover (call it trigger1) and the best HA range (call it trigger2). So:
• buy_strategy_generator > populate_buy_trend
• # TRIGGERS
• if 'trigger1' in params:
•                 if params['trigger1'] == 'ema3x5':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema5']))
•                 if params['trigger1'] == 'ema3x10':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema10']))
• if 'trigger2' in params:
•                 if params['trigger2'] == 'ha_close>ema3':
•                     conditions.append(dataframe['ha_close'] > dataframe['ema3'])
•                 if params['trigger2'] == 'ha_close>ema5':
•                     conditions.append(dataframe['ha_close'] > dataframe['ema5'])
• indicator_space
• Categorical(['ema3x5', 'ema3x10'], name='trigger1'),
• Categorical(['ha_close>ema3', 'ha_close>ema5'], name='trigger2'),
• For signals which involve "OR" (compared to the usual "AND")
• focus on setting up individual conditions correctly first (using "AND")
• can be verified with a backtest
• finally, introduce the "OR" 
• cannot be verified with a backtest

How to read Categorical results

• IF sell_indicator_space :
• Categorical([True, False], name='sell-sar_reversal-enabled'),                       
• Categorical([True, False], name='sell-macd_cross_signal-enabled'),                  
• Categorical(['sell-macd_cross_signal', 'sell-sar_reversal'], name='sell-trigger3')  
• CASE1
• Hyperopt result:
• 'sell-macd_cross_signal-enabled': True,
• 'sell-sar_reversal-enabled': True,
• 'sell-trigger3': 'sell-macd_cross_signal'
• This means:
• turn on macd
• turn on sar
• so that your (verification) backtest will be accurate.
• CASE2
• Hyperopt result:
• 'sell-macd_cross_signal-enabled': False,
• 'sell-sar_reversal-enabled': True,
• 'sell-trigger3': 'sell-macd_cross_signal'
• THIS MEANS:
• turn on macd
• turn on sar
• so that your (verification) backtest will be accurate.
• CASE3
• Hyperopt result:
• 'sell-macd_cross_signal-enabled': False,
• 'sell-sar_reversal-enabled': True,
• 'sell-trigger3': 'sell-sar_reversal'
• THIS MEANS:
• turn off macd
• turn on sar
• so that your (verification) backtest will be accurate.
• CONCLUSION: 
• If an indicator is labelled either "True" or a "sell-trigger", then turn it on. Ignore any "False" or "not a sell-trigger" label that exists simultaneously for that indicator.

Optimizing Bollinger bands (confirmed works? yes)

• strategy file
• populate_indicators
• bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
•         dataframe['bb_lowerband2'] = bollinger2['lower']
•         dataframe['bb_middleband2'] = bollinger2['mid']
•         dataframe['bb_upperband2'] = bollinger2['upper']
• bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
•         dataframe['bb_lowerband3'] = bollinger3['lower']
•         dataframe['bb_middleband3'] = bollinger3['mid']
•         dataframe['bb_upperband3'] = bollinger3['upper']
• bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
•         dataframe['bb_lowerband4'] = bollinger4['lower']
•         dataframe['bb_middleband4'] = bollinger4['mid']
•         dataframe['bb_upperband4'] = bollinger4['upper']
• populate_buy_trend (if required by strategy)
• (dataframe['close'] < dataframe['bb_lowerband2'])
• populate_sell_trend (if required by strategy)
• (dataframe['close'] > dataframe['bb_lowerband1'])
• hyperopt file
• buy side (if required by strategy)
• buy_strategy_generator > populate_buy_trend
• # TRIGGERS
• if 'trigger' in params:
•                 if params['trigger'] == 'bb_lower2':
•                     conditions.append(dataframe['close'] < dataframe['bb_lowerband2'])
•                 if params['trigger'] == 'bb_lower3':
•                     conditions.append(dataframe['close'] < dataframe['bb_lowerband3'])
•                 if params['trigger'] == 'bb_lower4':
•                     conditions.append(dataframe['close'] < dataframe['bb_lowerband4'])
• indicator_space
• Categorical(['bb_lower2', 'bb_lower3', 'bb_lower4'], name='trigger')
• sell side (if required by strategy)
• sell_strategy_generator > populate_sell_trend
• (similar to populate_buy_trend)
• sell_indicator_space
• (similar to indicator_space)

Optimising RSI (confirmed works? yes)

• strategy file
• populate_indicators
• dataframe['rsi'] = ta.RSI(dataframe)
• populate_buy_trend (if required by strategy)
• (dataframe['rsi'] < 20) & (use guard if inequality?)
• populate_sell_trend (if required by strategy)
• (qtpylib.crossed_above(dataframe['rsi'], 98)) & (use trigger if cross?)
• hyperopt file
• buy side (if required by strategy)
• buy_strategy_generator > populate_buy_trend
• # GUARDS AND TRENDS
• if params.get('rsi-enabled'):
•                 conditions.append(dataframe['rsi'] < params['rsi-value'])
• indicator_space
• Integer(5, 50, name='rsi-value'),
• Categorical([True, False], name='rsi-enabled'),
• sell side (if required by strategy)
• sell_strategy_generator > populate_sell_trend
• #TRIGGERS
• if params.get('sell-rsi-enabled'):
•                 conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
• sell_indicator_space
• Integer(60, 100, name='sell-rsi-value'),
• Categorical([True, False], name='sell-rsi-enabled'),

Optimising Stochastic Indicator's "SlowK"  (confirmed works? yes)

• strategy file
• populate_indicators
• stoch = ta.STOCH(dataframe)
• dataframe['slowd'] = stoch['slowd']
• dataframe['slowk'] = stoch['slowk']
• populate_buy_trend (if required by strategy)
• (dataframe['slowk'] < 20) &
• hyperopt file
• buy side (if required by strategy)
• buy_strategy_generator > populate_buy_trend
• # GUARDS AND TRENDS
• if params.get('slowk-enabled'):
•                 conditions.append(dataframe['slowk'] < params['slowk-value'])
• indicator_space
• Integer(0, 50, name='slowk-value'),
• Categorical([True, False], name='slowk-enabled'),

Optimising CDLHAMMER (CanDLestick Hammer) (confirmed works? yes)

Ref1: https://www.ig.com/uk/trading-strategies/16-candlestick-patterns-every-trader-should-know-180615

Ref2: https://quantlabs.net/blog/2018/06/reliable-candlestick-patterns-for-ta-lib-pattern-recognition-acts-as-a-bullish-reversal/

• strategy file
• populate_indicators
• dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
• populate_buy_trend 
• (dataframe['CDLHAMMER'] == 100) &
• hyperopt file
• buy side (if required by strategy)
• buy_strategy_generator > populate_buy_trend
• #GUARDS
• if params.get('CDLHAMMER-enabled'):
•                 conditions.append(dataframe['CDLHAMMER'] == 100)
• indicator_space
• Categorical([True, False], name='CDLHAMMER-enabled'),

Optimising Parabolic SAR (stop and reverse) (confirmed works? yes)

• strategy file
• populate_indicators
• dataframe['sar'] = ta.SAR(dataframe)
• populate_sell_trend
• (qtpylib.crossed_above(dataframe['sar'], dataframe['close'])) &
• hyperopt file
• buy side (if required by strategy) (untested)
• buy_strategy_generator > populate_buy_trend
• # TRIGGERS
• if 'trigger' in params:
•                 if params['trigger'] == 'sar_reversal':
•                     conditions.append(qtpylib.crossed_above(
•                         dataframe['close'], dataframe['sar']
•                     ))
• sell side (if required by strategy) (confirmed works? yes)
• sell_strategy_generator > populate_sell_trend
• #GUARDS
• if params.get('sell-sar_reversal-enabled'):
•                 conditions.append(qtpylib.crossed_above(dataframe['sar'], dataframe['close']))
• sell_indicator_space
• Categorical([True, False], name='sell-sar_reversal-enabled'),

Optimising Fisher RSI (confirmed works? yes)

• strategy file
• populate_indicators
• adj_rsi = 0.1 * (dataframe['rsi'] - 50)
• dataframe['fisher_rsi'] = (np.exp(2 * adj_rsi) - 1) / (np.exp(2 * adj_rsi) + 1)
• populate_sell_trend 
• (dataframe['fisher_rsi'] > 0.3) &
• hyperopt file
• sell side (if required by strategy) (confirmed works? yes)
• sell_strategy_generator > populate_sell_trend
• # GUARDS AND TRENDS
• if params.get('sell-fisher_rsi-enabled'):
•                 conditions.append(dataframe['fisher_rsi'] > params['sell-fisher_rsi-value'])
• sell_indicator_space (confirmed works? yes)
• Real(-0.99990920426, +0.99990920426, name='sell-fisher_rsi-value'),
• Categorical([True, False], name='sell-fisher_rsi-enabled'),
• After hyperopt process is completed: (1) Note the optimized "rsi" value in the hyperopt results (e.g. 'rsi-value': 30,); then (2) manually calculate the optimized "fisher_rsi" (i.e. fisher_rsi=-0.9640275801) (use https://www.desmos.com/scientific); then (3) update the value in "populate_sell_trend" (i.e. (dataframe['fisher_rsi'] > -0.9640275801) &)

Optimising EMA (exponential moving average) (confirmed works? yes)

• strategy file
• populate_indicators
• dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
• dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
• dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
• dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
• dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
• dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
• populate_buy_trend (if required by strategy)
• qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']) &
• populate_sell_trend (if required by strategy)
• qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) &
• hyperopt file
• buy side (if required by strategy)
• buy_strategy_generator > populate_buy_trend
• # TRIGGERS
• if 'trigger' in params:
•                 if params['trigger'] == 'ema3x5':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema5']))
•                 if params['trigger'] == 'ema3x10':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema10']))
•                 if params['trigger'] == 'ema3x20':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema20']))
•                 if params['trigger'] == 'ema3x50':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema50']))
•                 if params['trigger'] == 'ema3x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema100']))
•                 if params['trigger'] == 'ema5x10':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10']))
•                 if params['trigger'] == 'ema5x20':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema20']))
•                 if params['trigger'] == 'ema5x50':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema50']))
•                 if params['trigger'] == 'ema5x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema100']))
•                 if params['trigger'] == 'ema10x20':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema10'], dataframe['ema20']))
•                 if params['trigger'] == 'ema10x50':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema10'], dataframe['ema50']))
•                 if params['trigger'] == 'ema10x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema10'], dataframe['ema100']))
•                    if params['trigger'] == 'ema20x50':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']))
•                    if params['trigger'] == 'ema20x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema20'], dataframe['ema100']))
•                    if params['trigger'] == 'ema50x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']))
• indicator_space
• Categorical(['ema3x5', 'ema3x10', 'ema3x20', 'ema3x50', 'ema3x100', 'ema5x10', 'ema5x20', 'ema5x50', 'ema5x100', 'ema10x20', 'ema10x50', 'ema10x100', 'ema20x50', 'ema20x100', 'ema50x100'], name='trigger')
• sell side (if required by strategy)
• sell_strategy_generator > populate_sell_trend
• # TRIGGERS
• if 'sell-trigger' in params:
•                 if params['sell-trigger'] == 'ema3x5':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema5']))
•                 if params['sell-trigger'] == 'ema3x10':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema10']))
•                 if params['sell-trigger'] == 'ema3x20':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema20']))
•                 if params['sell-trigger'] == 'ema3x50':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema50']))
•                 if params['sell-trigger'] == 'ema3x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema100']))
•                 if params['sell-trigger'] == 'ema5x10':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10']))
•                 if params['sell-trigger'] == 'ema5x20':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema20']))
•                 if params['sell-trigger'] == 'ema5x50':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema50']))
•                 if params['sell-trigger'] == 'ema5x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema100']))
•                 if params['sell-trigger'] == 'ema10x20':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema10'], dataframe['ema20']))
•                 if params['sell-trigger'] == 'ema10x50':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema10'], dataframe['ema50']))
•                 if params['sell-trigger'] == 'ema10x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema10'], dataframe['ema100']))
•                    if params['sell-trigger'] == 'ema20x50':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']))
•                    if params['sell-trigger'] == 'ema20x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema20'], dataframe['ema100']))
•                    if params['sell-trigger'] == 'ema50x100':
•                     conditions.append(qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']))
• sell_indicator_space
• Categorical(['ema3x5', 'ema3x10', 'ema3x20', 'ema3x50', 'ema3x100', 'ema5x10', 'ema5x20', 'ema5x50', 'ema5x100', 'ema10x20', 'ema10x50', 'ema10x100', 'ema20x50', 'ema20x100', 'ema50x100'], name='sell-trigger')

PC acting weird

•  Monitor shows "no signal"
• graphic card spoilt (confirm by pluggin monitor into another PC)
• cable spoilt (confirm by using different cable)
• monitor spoilt (confirm by using different monitor)
• No electricity supply to Keyboard/mouse (which uses USB port) 
• Test keyboard/mouse on a different PC
• Test a different keyboard/mouse on the current PC
• Could be casing problem? (bring to hardware shop to check connections)